dgl.data¶

The dgl.data package contains datasets hosted by DGL and also utilities for downloading, processing, saving and loading data from external resources.

Quick links:

Node Prediction Datasets
Edge Prediction Datasets
Graph Prediction Datasets

Base Dataset Class¶

class dgl.data.DGLDataset(name, url=None, raw_dir=None, save_dir=None, hash_key=(), force_reload=False, verbose=False)[source]¶

The basic DGL dataset for creating graph datasets. This class defines a basic template class for DGL Dataset. The following steps will be executed automatically:

Check whether there is a dataset cache on disk (already processed and stored on the disk) by invoking has_cache(). If true, goto 5.

Call download() to download the data.

Call process() to process the data.

Call save() to save the processed dataset on disk and goto 6.

Call load() to load the processed dataset from disk.

Done.

Users can overwite these functions with their own data processing logic.

Parameters

name (str) – Name of the dataset
url (str) – Url to download the raw dataset
raw_dir (str) – Specifying the directory that will store the downloaded data or the directory that already stores the input data. Default: ~/.dgl/
save_dir (str) – Directory to save the processed dataset. Default: same as raw_dir
hash_key (tuple) – A tuple of values as the input for the hash function. Users can distinguish instances (and their caches on the disk) from the same dataset class by comparing the hash values. Default: (), the corresponding hash value is 'f9065fa7'.
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information

url¶

The URL to download the dataset

Type: str

name¶

The dataset name

Type: str

raw_dir¶

Raw file directory contains the input data folder

Type: str

raw_path¶

Directory contains the input data files. Default : os.path.join(self.raw_dir, self.name)

Type: str

save_dir¶

Directory to save the processed dataset

Type: str

save_path¶

File path to save the processed dataset

Type: str

verbose¶

Whether to print information

Type: bool

hash¶

Hash value for the dataset and the setting.

Type: str

abstract __getitem__(idx)[source]¶: Gets the data object at index.

abstract __len__()[source]¶: The number of examples in the dataset.

download()[source]¶

Overwite to realize your own logic of downloading data.

It is recommended to download the to the self.raw_dir folder. Can be ignored if the dataset is already in self.raw_dir.

has_cache()[source]¶

Overwrite to realize your own logic of deciding whether there exists a cached dataset.

By default False.

load()[source]¶

Overwite to realize your own logic of loading the saved dataset from files.

It is recommended to use dgl.data.utils.load_graphs to load dgl graph from files and use dgl.data.utils.load_info to load extra information into python dict object.

process()[source]¶: Overwrite to realize your own logic of processing the input data.

save()[source]¶

Overwite to realize your own logic of saving the processed dataset into files.

It is recommended to use dgl.data.utils.save_graphs to save dgl graph into files and use dgl.data.utils.save_info to save extra information into files.

Node Prediction Datasets¶

DGL hosted datasets for node classification/regression tasks.

Stanford sentiment treebank dataset¶

class dgl.data.SSTDataset(mode='train', glove_embed_file=None, vocab_file=None, raw_dir=None, force_reload=False, verbose=False)[source]¶

Stanford Sentiment Treebank dataset.

Deprecated since version 0.5.0:

trees is deprecated, it is replaced by:

>>> dataset = SSTDataset()
>>> for tree in dataset:
....    # your code here

num_vocabs is deprecated, it is replaced by vocab_size.

Each sample is the constituency tree of a sentence. The leaf nodes represent words. The word is a int value stored in the x feature field. The non-leaf node has a special value PAD_WORD in the x field. Each node also has a sentiment annotation: 5 classes (very negative, negative, neutral, positive and very positive). The sentiment label is a int value stored in the y feature field. Official site: http://nlp.stanford.edu/sentiment/index.html

Statistics:

Train examples: 8,544
Dev examples: 1,101
Test examples: 2,210
Number of classes for each node: 5

Parameters

mode (str, optional) – Should be one of [‘train’, ‘dev’, ‘test’, ‘tiny’] Default: train
glove_embed_file (str, optional) – The path to pretrained glove embedding file. Default: None
vocab_file (str, optional) – Optional vocabulary file. If not given, the default vacabulary file is used. Default: None
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

vocab¶

Vocabulary of the dataset

Type: OrderedDict

trees¶

A list of DGLGraph objects

Type: list

num_classes¶

Number of classes for each node

Type: int

pretrained_emb¶

Pretrained glove embedding with respect the vocabulary.

Type: Tensor

vocab_size¶

The size of the vocabulary

Type: int

num_vocabs¶

The size of the vocabulary

Type: int

Notes

All the samples will be loaded and preprocessed in the memory first.

Examples

>>> # get dataset
>>> train_data = SSTDataset()
>>> dev_data = SSTDataset(mode='dev')
>>> test_data = SSTDataset(mode='test')
>>> tiny_data = SSTDataset(mode='tiny')
>>>
>>> len(train_data)
8544
>>> train_data.num_classes
5
>>> glove_embed = train_data.pretrained_emb
>>> train_data.vocab_size
19536
>>> train_data[0]
Graph(num_nodes=71, num_edges=70,
  ndata_schemes={'x': Scheme(shape=(), dtype=torch.int64), 'y': Scheme(shape=(), dtype=torch.int64), 'mask': Scheme(shape=(), dtype=torch.int64)}
  edata_schemes={})
>>> for tree in train_data:
...     input_ids = tree.ndata['x']
...     labels = tree.ndata['y']
...     mask = tree.ndata['mask']
...     # your code here

__getitem__(idx)[source]¶

Get graph by index

Parameters

idx (int) –

Returns

graph structure, word id for each node, node labels and masks.

ndata['x']: word id of the node
ndata['y']: label of the node
ndata['mask']: 1 if the node is a leaf, otherwise 0

Return type

dgl.DGLGraph

__len__()[source]¶: Number of graphs in the dataset.

Karate club dataset¶

class dgl.data.KarateClubDataset[source]¶

Karate Club dataset for Node Classification

Deprecated since version 0.5.0:

data is deprecated, it is replaced by:

>>> dataset = KarateClubDataset()
>>> g = dataset[0]

Zachary’s karate club is a social network of a university karate club, described in the paper “An Information Flow Model for Conflict and Fission in Small Groups” by Wayne W. Zachary. The network became a popular example of community structure in networks after its use by Michelle Girvan and Mark Newman in 2002. Official website: http://konect.cc/networks/ucidata-zachary/

Karate Club dataset statistics:

Nodes: 34
Edges: 156
Number of Classes: 2

num_classes¶

Number of node classes

Type: int

data¶

A list of dgl.DGLGraph objects

Type: list

Examples

>>> dataset = KarateClubDataset()
>>> num_classes = dataset.num_classes
>>> g = dataset[0]
>>> labels = g.ndata['label']

__getitem__(idx)[source]¶

Get graph object

Parameters

idx (int) – Item index, KarateClubDataset has only one graph object

Returns

graph structure and labels.

ndata['label']: ground truth labels

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

Citation network dataset¶

class dgl.data.CoraGraphDataset(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True)[source]¶

Cora citation network dataset.

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = CoraGraphDataset()
>>> graph = dataset[0]

train_mask is deprecated, it is replaced by:

>>> dataset = CoraGraphDataset()
>>> graph = dataset[0]
>>> train_mask = graph.ndata['train_mask']

val_mask is deprecated, it is replaced by:

>>> dataset = CoraGraphDataset()
>>> graph = dataset[0]
>>> val_mask = graph.ndata['val_mask']

test_mask is deprecated, it is replaced by:

>>> dataset = CoraGraphDataset()
>>> graph = dataset[0]
>>> test_mask = graph.ndata['test_mask']

labels is deprecated, it is replaced by:

>>> dataset = CoraGraphDataset()
>>> graph = dataset[0]
>>> labels = graph.ndata['label']

feat is deprecated, it is replaced by:

>>> dataset = CoraGraphDataset()
>>> graph = dataset[0]
>>> feat = graph.ndata['feat']

Nodes mean paper and edges mean citation relationships. Each node has a predefined feature with 1433 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain paper.

Statistics:

Nodes: 2708
Edges: 10556
Number of Classes: 7
Label split:
- Train: 140
- Valid: 500
- Test: 1000

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.

num_classes¶

Number of label classes

Type: int

graph¶

Graph structure

Type: networkx.DiGraph

train_mask¶

Mask of training nodes

Type: numpy.ndarray

val_mask¶

Mask of validation nodes

Type: numpy.ndarray

test_mask¶

Mask of test nodes

Type: numpy.ndarray

labels¶

Ground truth labels of each node

Type: numpy.ndarray

features¶

Node features

Type: Tensor

Notes

The node feature is row-normalized.

Examples

>>> dataset = CoraGraphDataset()
>>> g = dataset[0]
>>> num_class = dataset.num_classes
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, CoraGraphDataset has only one graph object

Returns

graph structure, node features and labels.

ndata['train_mask']： mask for training node set
ndata['val_mask']: mask for validation node set
ndata['test_mask']: mask for test node set
ndata['feat']: node feature
ndata['label']: ground truth labels

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

class dgl.data.CiteseerGraphDataset(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True)[source]¶

Citeseer citation network dataset.

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = CiteseerGraphDataset()
>>> graph = dataset[0]

train_mask is deprecated, it is replaced by:

>>> dataset = CiteseerGraphDataset()
>>> graph = dataset[0]
>>> train_mask = graph.ndata['train_mask']

val_mask is deprecated, it is replaced by:

>>> dataset = CiteseerGraphDataset()
>>> graph = dataset[0]
>>> val_mask = graph.ndata['val_mask']

test_mask is deprecated, it is replaced by:

>>> dataset = CiteseerGraphDataset()
>>> graph = dataset[0]
>>> test_mask = graph.ndata['test_mask']

labels is deprecated, it is replaced by:

>>> dataset = CiteseerGraphDataset()
>>> graph = dataset[0]
>>> labels = graph.ndata['label']

feat is deprecated, it is replaced by:

>>> dataset = CiteseerGraphDataset()
>>> graph = dataset[0]
>>> feat = graph.ndata['feat']

Nodes mean scientific publications and edges mean citation relationships. Each node has a predefined feature with 3703 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain publication.

Statistics:

Nodes: 3327
Edges: 9228
Number of Classes: 6
Label Split:
- Train: 120
- Valid: 500
- Test: 1000

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.

num_classes¶

Number of label classes

Type: int

graph¶

Graph structure

Type: networkx.DiGraph

train_mask¶

Mask of training nodes

Type: numpy.ndarray

val_mask¶

Mask of validation nodes

Type: numpy.ndarray

test_mask¶

Mask of test nodes

Type: numpy.ndarray

labels¶

Ground truth labels of each node

Type: numpy.ndarray

features¶

Node features

Type: Tensor

Notes

The node feature is row-normalized.

In citeseer dataset, there are some isolated nodes in the graph. These isolated nodes are added as zero-vecs into the right position.

Examples

>>> dataset = CiteseerGraphDataset()
>>> g = dataset[0]
>>> num_class = dataset.num_classes
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, CiteseerGraphDataset has only one graph object

Returns

graph structure, node features and labels.

ndata['train_mask']： mask for training node set
ndata['val_mask']: mask for validation node set
ndata['test_mask']: mask for test node set
ndata['feat']: node feature
ndata['label']: ground truth labels

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

class dgl.data.PubmedGraphDataset(raw_dir=None, force_reload=False, verbose=True, reverse_edge=True)[source]¶

Pubmed citation network dataset.

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = PubmedGraphDataset()
>>> graph = dataset[0]

train_mask is deprecated, it is replaced by:

>>> dataset = PubmedGraphDataset()
>>> graph = dataset[0]
>>> train_mask = graph.ndata['train_mask']

val_mask is deprecated, it is replaced by:

>>> dataset = PubmedGraphDataset()
>>> graph = dataset[0]
>>> val_mask = graph.ndata['val_mask']

test_mask is deprecated, it is replaced by:

>>> dataset = PubmedGraphDataset()
>>> graph = dataset[0]
>>> test_mask = graph.ndata['test_mask']

labels is deprecated, it is replaced by:

>>> dataset = PubmedGraphDataset()
>>> graph = dataset[0]
>>> labels = graph.ndata['label']

feat is deprecated, it is replaced by:

>>> dataset = PubmedGraphDataset()
>>> graph = dataset[0]
>>> feat = graph.ndata['feat']

Nodes mean scientific publications and edges mean citation relationships. Each node has a predefined feature with 500 dimensions. The dataset is designed for the node classification task. The task is to predict the category of certain publication.

Statistics:

Nodes: 19717
Edges: 88651
Number of Classes: 3
Label Split:
- Train: 60
- Valid: 500
- Test: 1000

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.
reverse_edge (bool) – Whether to add reverse edges in graph. Default: True.

num_classes¶

Number of label classes

Type: int

graph¶

Graph structure

Type: networkx.DiGraph

train_mask¶

Mask of training nodes

Type: numpy.ndarray

val_mask¶

Mask of validation nodes

Type: numpy.ndarray

test_mask¶

Mask of test nodes

Type: numpy.ndarray

labels¶

Ground truth labels of each node

Type: numpy.ndarray

features¶

Node features

Type: Tensor

Notes

The node feature is row-normalized.

Examples

>>> dataset = PubmedGraphDataset()
>>> g = dataset[0]
>>> num_class = dataset.num_of_class
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, PubmedGraphDataset has only one graph object

Returns

graph structure, node features and labels.

ndata['train_mask']： mask for training node set
ndata['val_mask']: mask for validation node set
ndata['test_mask']: mask for test node set
ndata['feat']: node feature
ndata['label']: ground truth labels

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

CoraFull dataset¶

class dgl.data.CoraFullDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

CORA-Full dataset for node classification task.

Deprecated since version 0.5.0:

data is deprecated, it is repalced by:

>>> dataset = CoraFullDataset()
>>> graph = dataset[0]

Extended Cora dataset. Nodes represent paper and edges represent citations.

Reference: https://github.com/shchur/gnn-benchmark#datasets

Statistics:

Nodes: 19,793
Edges: 126,842 (note that the original dataset has 65,311 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of Classes: 70
Node feature size: 8,710

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes for each node.

Type: int

data¶

A list of DGLGraph objects

Type: list

Examples

>>> data = CoraFullDataset()
>>> g = data[0]
>>> num_class = data.num_classes
>>> feat = g.ndata['feat']  # get node feature
>>> label = g.ndata['label']  # get node labels

__getitem__(idx)¶

Get graph by index

Parameters

idx (int) – Item index

Returns

The graph contains:

ndata['feat']: node features
ndata['label']: node labels

Return type

dgl.DGLGraph

__len__()¶: Number of graphs in the dataset

RDF datasets¶

class dgl.data.AIFBDataset(print_every=10000, insert_reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

AIFB dataset for node classification task

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = AIFBDataset()
>>> graph = dataset[0]

train_idx is deprecated, it can be replaced by:

>>> dataset = AIFBDataset()
>>> graph = dataset[0]
>>> train_mask = graph.nodes[dataset.category].data['train_mask']
>>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()

test_idx is deprecated, it can be replaced by:

>>> dataset = AIFBDataset()
>>> graph = dataset[0]
>>> test_mask = graph.nodes[dataset.category].data['test_mask']
>>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()

AIFB DataSet is a Semantic Web (RDF) dataset used as a benchmark in data mining. It records the organizational structure of AIFB at the University of Karlsruhe.

AIFB dataset statistics:

Nodes: 7262
Edges: 48810 (including reverse edges)
Target Category: Personen
Number of Classes: 4
Label Split:
- Train: 140
- Test: 36

Parameters

print_every (int) – Preprocessing log for every X tuples. Default: 10000.
insert_reverse (bool) – If true, add reverse edge and reverse relations to the final graph. Default: True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes to predict

Type: int

predict_category¶

The entity category (node type) that has labels for prediction

Type: str

labels¶

All the labels of the entities in predict_category

Type: Tensor

graph¶

Graph structure

Type: dgl.DGLGraph

train_idx¶

Entity IDs for training. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

test_idx¶

Entity IDs for testing. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

Examples

>>> dataset = dgl.data.rdf.AIFBDataset()
>>> graph = dataset[0]
>>> category = dataset.predict_category
>>> num_classes = dataset.num_classes
>>>
>>> train_mask = g.nodes[category].data.pop('train_mask')
>>> test_mask = g.nodes[category].data.pop('test_mask')
>>> labels = g.nodes[category].data.pop('labels')

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, AIFBDataset has only one graph object

Returns

The graph contains:

ndata['train_mask']: mask for training node set
ndata['test_mask']: mask for testing node set
ndata['labels']: mask for labels

Return type

dgl.DGLGraph

__len__()[source]¶

The number of graphs in the dataset.

Returns
Return type: int

class dgl.data.MUTAGDataset(print_every=10000, insert_reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

MUTAG dataset for node classification task

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = MUTAGDataset()
>>> graph = dataset[0]

train_idx is deprecated, it can be replaced by:

>>> dataset = MUTAGDataset()
>>> graph = dataset[0]
>>> train_mask = graph.nodes[dataset.category].data['train_mask']
>>> train_idx = th.nonzero(train_mask).squeeze()

test_idx is deprecated, it can be replaced by:

>>> dataset = MUTAGDataset()
>>> graph = dataset[0]
>>> test_mask = graph.nodes[dataset.category].data['test_mask']
>>> test_idx = th.nonzero(test_mask).squeeze()

Mutag dataset statistics:

Nodes: 27163
Edges: 148100 (including reverse edges)
Target Category: d
Number of Classes: 2
Label Split:
- Train: 272
- Test: 68

Parameters

print_every (int) – Preprocessing log for every X tuples. Default: 10000.
insert_reverse (bool) – If true, add reverse edge and reverse relations to the final graph. Default: True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes to predict

Type: int

predict_category¶

The entity category (node type) that has labels for prediction

Type: str

labels¶

All the labels of the entities in predict_category

Type: Tensor

graph¶

Graph structure

Type: dgl.DGLGraph

train_idx¶

Entity IDs for training. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

test_idx¶

Entity IDs for testing. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

Examples

>>> dataset = dgl.data.rdf.MUTAGDataset()
>>> graph = dataset[0]
>>> category = dataset.predict_category
>>> num_classes = dataset.num_classes
>>>
>>> train_mask = g.nodes[category].data.pop('train_mask')
>>> test_mask = g.nodes[category].data.pop('test_mask')
>>> labels = g.nodes[category].data.pop('labels')

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, MUTAGDataset has only one graph object

Returns

The graph contains:

ndata['train_mask']: mask for training node set
ndata['test_mask']: mask for testing node set
ndata['labels']: mask for labels

Return type

dgl.DGLGraph

__len__()[source]¶

The number of graphs in the dataset.

Returns
Return type: int

class dgl.data.BGSDataset(print_every=10000, insert_reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

BGS dataset for node classification task

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = BGSDataset()
>>> graph = dataset[0]

train_idx is deprecated, it can be replaced by:

>>> dataset = BGSDataset()
>>> graph = dataset[0]
>>> train_mask = graph.nodes[dataset.category].data['train_mask']
>>> train_idx = th.nonzero(train_mask).squeeze()

test_idx is deprecated, it can be replaced by:

>>> dataset = BGSDataset()
>>> graph = dataset[0]
>>> test_mask = graph.nodes[dataset.category].data['test_mask']
>>> test_idx = th.nonzero(test_mask).squeeze()

BGS namespace convention: http://data.bgs.ac.uk/(ref|id)/<Major Concept>/<Sub Concept>/INSTANCE. We ignored all literal nodes and the relations connecting them in the output graph. We also ignored the relation used to mark whether a term is CURRENT or DEPRECATED.

BGS dataset statistics:

Nodes: 94806
Edges: 672884 (including reverse edges)
Target Category: Lexicon/NamedRockUnit
Number of Classes: 2
Label Split:
- Train: 117
- Test: 29

Parameters

print_every (int) – Preprocessing log for every X tuples. Default: 10000.
insert_reverse (bool) – If true, add reverse edge and reverse relations to the final graph. Default: True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes to predict

Type: int

predict_category¶

The entity category (node type) that has labels for prediction

Type: str

labels¶

All the labels of the entities in predict_category

Type: Tensor

graph¶

Graph structure

Type: dgl.DGLGraph

train_idx¶

Entity IDs for training. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

test_idx¶

Entity IDs for testing. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

Examples

>>> dataset = dgl.data.rdf.BGSDataset()
>>> graph = dataset[0]
>>> category = dataset.predict_category
>>> num_classes = dataset.num_classes
>>>
>>> train_mask = g.nodes[category].data.pop('train_mask')
>>> test_mask = g.nodes[category].data.pop('test_mask')
>>> labels = g.nodes[category].data.pop('labels')

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, BGSDataset has only one graph object

Returns

The graph contains:

ndata['train_mask']: mask for training node set
ndata['test_mask']: mask for testing node set
ndata['labels']: mask for labels

Return type

dgl.DGLGraph

__len__()[source]¶

The number of graphs in the dataset.

Returns
Return type: int

class dgl.data.AMDataset(print_every=10000, insert_reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

AM dataset. for node classification task

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = AMDataset()
>>> graph = dataset[0]

train_idx is deprecated, it can be replaced by:

>>> dataset = AMDataset()
>>> graph = dataset[0]
>>> train_mask = graph.nodes[dataset.category].data['train_mask']
>>> train_idx = th.nonzero(train_mask).squeeze()

test_idx is deprecated, it can be replaced by:

>>> dataset = AMDataset()
>>> graph = dataset[0]
>>> test_mask = graph.nodes[dataset.category].data['test_mask']
>>> test_idx = th.nonzero(test_mask).squeeze()

Namespace convention:

Instance: http://purl.org/collections/nl/am/<type>-<id>
Relation: http://purl.org/collections/nl/am/<name>

We ignored all literal nodes and the relations connecting them in the output graph.

AM dataset statistics:

Nodes: 881680
Edges: 5668682 (including reverse edges)
Target Category: proxy
Number of Classes: 11
Label Split:
- Train: 802
- Test: 198

Parameters

print_every (int) – Preprocessing log for every X tuples. Default: 10000.
insert_reverse (bool) – If true, add reverse edge and reverse relations to the final graph. Default: True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes to predict

Type: int

predict_category¶

The entity category (node type) that has labels for prediction

Type: str

labels¶

All the labels of the entities in predict_category

Type: Tensor

graph¶

Graph structure

Type: dgl.DGLGraph

train_idx¶

Entity IDs for training. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

test_idx¶

Entity IDs for testing. All IDs are local IDs w.r.t. to predict_category.

Type: Tensor

Examples

>>> dataset = dgl.data.rdf.AMDataset()
>>> graph = dataset[0]
>>> category = dataset.predict_category
>>> num_classes = dataset.num_classes
>>>
>>> train_mask = g.nodes[category].data.pop('train_mask')
>>> test_mask = g.nodes[category].data.pop('test_mask')
>>> labels = g.nodes[category].data.pop('labels')

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, AMDataset has only one graph object

Returns

The graph contains:

ndata['train_mask']: mask for training node set
ndata['test_mask']: mask for testing node set
ndata['labels']: mask for labels

Return type

dgl.DGLGraph

__len__()[source]¶

The number of graphs in the dataset.

Returns
Return type: int

Amazon Co-Purchase dataset¶

class dgl.data.AmazonCoBuyComputerDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

‘Computer’ part of the AmazonCoBuy dataset for node classification task.

Deprecated since version 0.5.0:

data is deprecated, it is repalced by:

>>> dataset = AmazonCoBuyComputerDataset()
>>> graph = dataset[0]

Amazon Computers and Amazon Photo are segments of the Amazon co-purchase graph [McAuley et al., 2015], where nodes represent goods, edges indicate that two goods are frequently bought together, node features are bag-of-words encoded product reviews, and class labels are given by the product category.

Reference: https://github.com/shchur/gnn-benchmark#datasets

Statistics:

Nodes: 13,752
Edges: 491,722 (note that the original dataset has 245,778 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of classes: 10
Node feature size: 767

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes for each node.

Type: int

data¶

A list of DGLGraph objects

Type: list

Examples

>>> data = AmazonCoBuyComputerDataset()
>>> g = data[0]
>>> num_class = data.num_classes
>>> feat = g.ndata['feat']  # get node feature
>>> label = g.ndata['label']  # get node labels

__getitem__(idx)¶

Get graph by index

Parameters

idx (int) – Item index

Returns

The graph contains:

ndata['feat']: node features
ndata['label']: node labels

Return type

dgl.DGLGraph

__len__()¶: Number of graphs in the dataset

class dgl.data.AmazonCoBuyPhotoDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

AmazonCoBuy dataset for node classification task.

Deprecated since version 0.5.0:

data is deprecated, it is repalced by:

>>> dataset = AmazonCoBuyPhotoDataset()
>>> graph = dataset[0]

Amazon Computers and Amazon Photo are segments of the Amazon co-purchase graph [McAuley et al., 2015], where nodes represent goods, edges indicate that two goods are frequently bought together, node features are bag-of-words encoded product reviews, and class labels are given by the product category.

Reference: https://github.com/shchur/gnn-benchmark#datasets

Statistics

Nodes: 7,650
Edges: 238,163 (note that the original dataset has 119,043 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of classes: 8
Node feature size: 745

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes for each node.

Type: int

data¶

A list of DGLGraph objects

Type: list

Examples

>>> data = AmazonCoBuyPhotoDataset()
>>> g = data[0]
>>> num_class = data.num_classes
>>> feat = g.ndata['feat']  # get node feature
>>> label = g.ndata['label']  # get node labels

__getitem__(idx)¶

Get graph by index

Parameters

idx (int) – Item index

Returns

The graph contains:

ndata['feat']: node features
ndata['label']: node labels

Return type

dgl.DGLGraph

__len__()¶: Number of graphs in the dataset

Coauthor dataset¶

class dgl.data.CoauthorCSDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

‘Computer Science (CS)’ part of the Coauthor dataset for node classification task.

Deprecated since version 0.5.0:

data is deprecated, it is repalced by:

>>> dataset = CoauthorCSDataset()
>>> graph = dataset[0]

Coauthor CS and Coauthor Physics are co-authorship graphs based on the Microsoft Academic Graph from the KDD Cup 2016 challenge. Here, nodes are authors, that are connected by an edge if they co-authored a paper; node features represent paper keywords for each author’s papers, and class labels indicate most active fields of study for each author.

Reference: https://github.com/shchur/gnn-benchmark#datasets

Statistics:

Nodes: 18,333
Edges: 163,788 (note that the original dataset has 81,894 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of classes: 15
Node feature size: 6,805

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes for each node.

Type: int

data¶

A list of DGLGraph objects

Type: list

Examples

>>> data = CoauthorCSDataset()
>>> g = data[0]
>>> num_class = data.num_classes
>>> feat = g.ndata['feat']  # get node feature
>>> label = g.ndata['label']  # get node labels

__getitem__(idx)¶

Get graph by index

Parameters

idx (int) – Item index

Returns

The graph contains:

ndata['feat']: node features
ndata['label']: node labels

Return type

dgl.DGLGraph

__len__()¶: Number of graphs in the dataset

class dgl.data.CoauthorPhysicsDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

‘Physics’ part of the Coauthor dataset for node classification task.

Deprecated since version 0.5.0:

data is deprecated, it is repalced by:

>>> dataset = CoauthorPhysicsDataset()
>>> graph = dataset[0]

Coauthor CS and Coauthor Physics are co-authorship graphs based on the Microsoft Academic Graph from the KDD Cup 2016 challenge. Here, nodes are authors, that are connected by an edge if they co-authored a paper; node features represent paper keywords for each author’s papers, and class labels indicate most active fields of study for each author.

Reference: https://github.com/shchur/gnn-benchmark#datasets

Statistics

Nodes: 34,493
Edges: 495,924 (note that the original dataset has 247,962 edges but DGL adds the reverse edges and remove the duplicates, hence with a different number)
Number of classes: 5
Node feature size: 8,415

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes for each node.

Type: int

data¶

A list of DGLGraph objects

Type: list

Examples

>>> data = CoauthorPhysicsDataset()
>>> g = data[0]
>>> num_class = data.num_classes
>>> feat = g.ndata['feat']  # get node feature
>>> label = g.ndata['label']  # get node labels

__getitem__(idx)¶

Get graph by index

Parameters

idx (int) – Item index

Returns

The graph contains:

ndata['feat']: node features
ndata['label']: node labels

Return type

dgl.DGLGraph

__len__()¶: Number of graphs in the dataset

Protein-Protein Interaction dataset¶

class dgl.data.PPIDataset(mode='train', raw_dir=None, force_reload=False, verbose=False)[source]¶

Protein-Protein Interaction dataset for inductive node classification

Deprecated since version 0.5.0:

lables is deprecated, it is replaced by:

>>> dataset = PPIDataset()
>>> for g in dataset:
....    labels = g.ndata['label']
....
>>>

features is deprecated, it is replaced by:

>>> dataset = PPIDataset()
>>> for g in dataset:
....    features = g.ndata['feat']
....
>>>

A toy Protein-Protein Interaction network dataset. The dataset contains 24 graphs. The average number of nodes per graph is 2372. Each node has 50 features and 121 labels. 20 graphs for training, 2 for validation and 2 for testing.

Reference: http://snap.stanford.edu/graphsage/

Statistics:

Train examples: 20
Valid examples: 2
Test examples: 2

Parameters

mode (str) – Must be one of (‘train’, ‘valid’, ‘test’). Default: ‘train’
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_labels¶

Number of labels for each node

Type: int

labels¶

Node labels

Type: Tensor

features¶

Node features

Type: Tensor

Examples

>>> dataset = PPIDataset(mode='valid')
>>> num_labels = dataset.num_labels
>>> for g in dataset:
....    feat = g.ndata['feat']
....    label = g.ndata['label']
....    # your code here
>>>

__getitem__(item)[source]¶

Get the item^th sample.

Parameters

item (int) – The sample index.

Returns

graph structure, node features and node labels.

ndata['feat']: node features
ndata['label']: node labels

Return type

dgl.DGLGraph

__len__()[source]¶: Return number of samples in this dataset.

Reddit dataset¶

class dgl.data.RedditDataset(self_loop=False, raw_dir=None, force_reload=False, verbose=False)[source]¶

Reddit dataset for community detection (node classification)

Deprecated since version 0.5.0:

graph is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]

num_labels is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> num_classes = dataset.num_classes

train_mask is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> train_mask = graph.ndata['train_mask']

val_mask is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> val_mask = graph.ndata['val_mask']

test_mask is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> test_mask = graph.ndata['test_mask']

features is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> features = graph.ndata['feat']

labels is deprecated, it is replaced by:

>>> dataset = RedditDataset()
>>> graph = dataset[0]
>>> labels = graph.ndata['label']

This is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. The authors sampled 50 large communities and built a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. We use the first 20 days for training and the remaining days for testing (with 30% used for validation).

Reference: http://snap.stanford.edu/graphsage/

Statistics

Nodes: 232,965
Edges: 114,615,892
Node feature size: 602
Number of training samples: 153,431
Number of validation samples: 23,831
Number of test samples: 55,703

Parameters

self_loop (bool) – Whether load dataset with self loop connections. Default: False
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of classes for each node

Type: int

graph¶

Graph of the dataset

Type: dgl.DGLGraph

num_labels¶

Number of classes for each node

Type: int

train_mask¶

Mask of training nodes

Type: numpy.ndarray

val_mask¶

Mask of validation nodes

Type: numpy.ndarray

test_mask¶

Mask of test nodes

Type: numpy.ndarray

features¶

Node features

Type: Tensor

labels¶

Node labels

Type: Tensor

Examples

>>> data = RedditDataset()
>>> g = data[0]
>>> num_classes = data.num_classes
>>>
>>> # get node feature
>>> feat = g.ndata['feat']
>>>
>>> # get data split
>>> train_mask = g.ndata['train_mask']
>>> val_mask = g.ndata['val_mask']
>>> test_mask = g.ndata['test_mask']
>>>
>>> # get labels
>>> label = g.ndata['label']
>>>
>>> # Train, Validation and Test

__getitem__(idx)[source]¶

Get graph by index

Parameters

idx (int) – Item index

Returns

graph structure, node labels, node features and splitting masks:

ndata['label']: node label
ndata['feat']: node feature
ndata['train_mask']： mask for training node set
ndata['val_mask']: mask for validation node set
ndata['test_mask']: mask for test node set

Return type

dgl.DGLGraph

__len__()[source]¶: Number of graphs in the dataset

Symmetric Stochastic Block Model Mixture dataset¶

class dgl.data.SBMMixtureDataset(n_graphs, n_nodes, n_communities, k=2, avg_deg=3, pq='Appendix_C', rng=None)[source]¶

Symmetric Stochastic Block Model Mixture

Reference: Appendix C of Supervised Community Detection with Hierarchical Graph Neural Networks

Parameters

n_graphs (int) – Number of graphs.
n_nodes (int) – Number of nodes.
n_communities (int) – Number of communities.
k (int, optional) – Multiplier. Default: 2
avg_deg (int, optional) – Average degree. Default: 3
pq (list of pair of nonnegative float or str, optional) – Random densities. This parameter is for future extension, for now it’s always using the default value. Default: Appendix_C
rng (numpy.random.RandomState, optional) – Random number generator. If not given, it’s numpy.random.RandomState() with seed=None, which read data from /dev/urandom (or the Windows analogue) if available or seed from the clock otherwise. Default: None

Raises

RuntimeError is raised if pq is not a list or string. –

Examples

>>> data = SBMMixtureDataset(n_graphs=16, n_nodes=10000, n_communities=2)
>>> from torch.utils.data import DataLoader
>>> dataloader = DataLoader(data, batch_size=1, collate_fn=data.collate_fn)
>>> for graph, line_graph, graph_degrees, line_graph_degrees, pm_pd in dataloader:
...     # your code here

__getitem__(idx)[source]¶

Get one example by index

Parameters

idx (int) – Item index

Returns

graph (dgl.DGLGraph) – The original graph
line_graph (dgl.DGLGraph) – The line graph of graph
graph_degree (numpy.ndarray) – In degrees for each node in graph
line_graph_degree (numpy.ndarray) – In degrees for each node in line_graph
pm_pd (numpy.ndarray) – Edge indicator matrices Pm and Pd

__len__()[source]¶: Number of graphs in the dataset.

collate_fn(x)[source]¶

The collate function for dataloader

Parameters

x (tuple) –

a batch of data that contains:

graph: dgl.DGLGraph
The original graph
line_graph: dgl.DGLGraph
The line graph of graph
graph_degree: numpy.ndarray
In degrees for each node in graph
line_graph_degree: numpy.ndarray
In degrees for each node in line_graph
pm_pd: numpy.ndarray
Edge indicator matrices Pm and Pd

Returns

g_batch (dgl.DGLGraph) – Batched graphs
lg_batch (dgl.DGLGraph) – Batched line graphs
degg_batch (numpy.ndarray) – A batch of in degrees for each node in g_batch
deglg_batch (numpy.ndarray) – A batch of in degrees for each node in lg_batch
pm_pd_batch (numpy.ndarray) – A batch of edge indicator matrices Pm and Pd

Fraud dataset¶

class dgl.data.FraudDataset(name, raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1, force_reload=False, verbose=True)[source]¶

Fraud node prediction dataset.

The dataset includes two multi-relational graphs extracted from Yelp and Amazon where nodes represent fraudulent reviews or fraudulent reviewers.

It was first proposed in a CIKM‘20 paper <https://arxiv.org/pdf/2008.08692.pdf> and has been used by a recent WWW‘21 paper <https://ponderly.github.io/pub/PCGNN_WWW2021.pdf> as a benchmark. Another paper <https://arxiv.org/pdf/2104.01404.pdf> also takes the dataset as an example to study the non-homophilous graphs. This dataset is built upon industrial data and has rich relational information and unique properties like class-imbalance and feature inconsistency, which makes the dataset be a good instance to investigate how GNNs perform on real-world noisy graphs. These graphs are bidirected and not self connected.

Reference: <https://github.com/YingtongDou/CARE-GNN>

Parameters

name (str) – Name of the dataset
raw_dir (str) – Specifying the directory that will store the downloaded data or the directory that already stores the input data. Default: ~/.dgl/
random_seed (int) – Specifying the random seed in splitting the dataset. Default: 717
train_size (float) – training set size of the dataset. Default: 0.7
val_size (float) – validation set size of the dataset, and the size of testing set is (1 - train_size - val_size) Default: 0.1
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_classes¶

Number of label classes

Type: int

graph¶

Graph structure, etc.

Type: dgl.DGLGraph

seed¶

Random seed in splitting the dataset.

Type: int

train_size¶

Training set size of the dataset.

Type: float

val_size¶

Validation set size of the dataset

Type: float

Examples

>>> dataset = FraudDataset('yelp')
>>> graph = dataset[0]
>>> num_classes = dataset.num_classes
>>> feat = graph.ndata['feature']
>>> label = graph.ndata['label']

__getitem__(idx)[source]¶

Get graph object

Parameters

idx (int) – Item index

Returns

graph structure, node features, node labels and masks

ndata['feature']: node features
ndata['label']: node labels
ndata['train_mask']: mask of training set
ndata['val_mask']: mask of validation set
ndata['test_mask']: mask of testing set

Return type

dgl.DGLGraph

__len__()[source]¶: number of data examples

class dgl.data.FraudYelpDataset(raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1, force_reload=False, verbose=True)[source]¶

Fraud Yelp Dataset

The Yelp dataset includes hotel and restaurant reviews filtered (spam) and recommended (legitimate) by Yelp. A spam review detection task can be conducted, which is a binary classification task. 32 handcrafted features from <http://dx.doi.org/10.1145/2783258.2783370> are taken as the raw node features. Reviews are nodes in the graph, and three relations are:

R-U-R: it connects reviews posted by the same user

R-S-R: it connects reviews under the same product with the same star rating (1-5 stars)

R-T-R: it connects two reviews under the same product posted in the same month.

Statistics:

Nodes: 45,954
Edges:
- R-U-R: 98,630
- R-T-R: 1,147,232
- R-S-R: 6,805,486
Classes:
- Positive (spam): 6,677
- Negative (legitimate): 39,277
Positive-Negative ratio: 1 : 5.9
Node feature size: 32

Parameters

raw_dir (str) – Specifying the directory that will store the downloaded data or the directory that already stores the input data. Default: ~/.dgl/
random_seed (int) – Specifying the random seed in splitting the dataset. Default: 717
train_size (float) – training set size of the dataset. Default: 0.7
val_size (float) – validation set size of the dataset, and the size of testing set is (1 - train_size - val_size) Default: 0.1
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

Examples

>>> dataset = FraudYelpDataset()
>>> graph = dataset[0]
>>> num_classes = dataset.num_classes
>>> feat = graph.ndata['feature']
>>> label = graph.ndata['label']

__getitem__(idx)¶

Get graph object

Parameters

idx (int) – Item index

Returns

graph structure, node features, node labels and masks

ndata['feature']: node features
ndata['label']: node labels
ndata['train_mask']: mask of training set
ndata['val_mask']: mask of validation set
ndata['test_mask']: mask of testing set

Return type

dgl.DGLGraph

__len__()¶: number of data examples

class dgl.data.FraudAmazonDataset(raw_dir=None, random_seed=717, train_size=0.7, val_size=0.1, force_reload=False, verbose=True)[source]¶

Fraud Amazon Dataset

The Amazon dataset includes product reviews under the Musical Instruments category. Users with more than 80% helpful votes are labelled as benign entities and users with less than 20% helpful votes are labelled as fraudulent entities. A fraudulent user detection task can be conducted on the Amazon dataset, which is a binary classification task. 25 handcrafted features from <https://arxiv.org/pdf/2005.10150.pdf> are taken as the raw node features .

Users are nodes in the graph, and three relations are: 1. U-P-U : it connects users reviewing at least one same product 2. U-S-U : it connects users having at least one same star rating within one week 3. U-V-U : it connects users with top 5% mutual review text similarities (measured by TF-IDF) among all users.

Statistics:

Nodes: 11,944
Edges:
- U-P-U: 351,216
- U-S-U: 7,132,958
- U-V-U: 2,073,474
Classes:
- Positive (fraudulent): 821
- Negative (benign): 7,818
- Unlabeled: 3,305
Positive-Negative ratio: 1 : 10.5
Node feature size: 25

Parameters

raw_dir (str) – Specifying the directory that will store the downloaded data or the directory that already stores the input data. Default: ~/.dgl/
random_seed (int) – Specifying the random seed in splitting the dataset. Default: 717
train_size (float) – training set size of the dataset. Default: 0.7
val_size (float) – validation set size of the dataset, and the size of testing set is (1 - train_size - val_size) Default: 0.1
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

Examples

>>> dataset = FraudAmazonDataset()
>>> graph = dataset[0]
>>> num_classes = dataset.num_classes
>>> feat = graph.ndata['feature']
>>> label = graph.ndata['label']

__getitem__(idx)¶

Get graph object

Parameters

idx (int) – Item index

Returns

graph structure, node features, node labels and masks

ndata['feature']: node features
ndata['label']: node labels
ndata['train_mask']: mask of training set
ndata['val_mask']: mask of validation set
ndata['test_mask']: mask of testing set

Return type

dgl.DGLGraph

__len__()¶: number of data examples

Edge Prediction Datasets¶

DGL hosted datasets for edge classification/regression and link prediction tasks.

Knowlege graph dataset¶

class dgl.data.FB15k237Dataset(reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

FB15k237 link prediction dataset.

Deprecated since version 0.5.0:

train is deprecated, it is replaced by:

>>> dataset = FB15k237Dataset()
>>> graph = dataset[0]
>>> train_mask = graph.edata['train_mask']
>>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(train_idx)
>>> rel = graph.edata['etype'][train_idx]

valid is deprecated, it is replaced by:

>>> dataset = FB15k237Dataset()
>>> graph = dataset[0]
>>> val_mask = graph.edata['val_mask']
>>> val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(val_idx)
>>> rel = graph.edata['etype'][val_idx]

test is deprecated, it is replaced by:

>>> dataset = FB15k237Dataset()
>>> graph = dataset[0]
>>> test_mask = graph.edata['test_mask']
>>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(test_idx)
>>> rel = graph.edata['etype'][test_idx]

FB15k-237 is a subset of FB15k where inverse relations are removed. When creating the dataset, a reverse edge with reversed relation types are created for each edge by default.

FB15k237 dataset statistics:

Nodes: 14541
Number of relation types: 237
Number of reversed relation types: 237
Label Split:
- Train: 272115
- Valid: 17535
- Test: 20466

Parameters

reverse (bool) – Whether to add reverse edge. Default True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_nodes¶

Number of nodes

Type: int

num_rels¶

Number of relation types

Type: int

train¶

A numpy array of triplets (src, rel, dst) for the training graph

Type: numpy.ndarray

valid¶

A numpy array of triplets (src, rel, dst) for the validation graph

Type: numpy.ndarray

test¶

A numpy array of triplets (src, rel, dst) for the test graph

Type: numpy.ndarray

Examples

>>> dataset = FB15k237Dataset()
>>> g = dataset.graph
>>> e_type = g.edata['e_type']
>>>
>>> # get data split
>>> train_mask = g.edata['train_mask']
>>> val_mask = g.edata['val_mask']
>>> test_mask = g.edata['test_mask']
>>>
>>> train_set = th.arange(g.number_of_edges())[train_mask]
>>> val_set = th.arange(g.number_of_edges())[val_mask]
>>>
>>> # build train_g
>>> train_edges = train_set
>>> train_g = g.edge_subgraph(train_edges,
                              relabel_nodes=False)
>>> train_g.edata['e_type'] = e_type[train_edges];
>>>
>>> # build val_g
>>> val_edges = th.cat([train_edges, val_edges])
>>> val_g = g.edge_subgraph(val_edges,
                            relabel_nodes=False)
>>> val_g.edata['e_type'] = e_type[val_edges];
>>>
>>> # Train, Validation and Test

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, FB15k237Dataset has only one graph object

Returns

The graph contains

edata['e_type']: edge relation type
edata['train_edge_mask']: positive training edge mask
edata['val_edge_mask']: positive validation edge mask
edata['test_edge_mask']: positive testing edge mask
edata['train_mask']: training edge set mask (include reversed training edges)
edata['val_mask']: validation edge set mask (include reversed validation edges)
edata['test_mask']: testing edge set mask (include reversed testing edges)
ndata['ntype']: node type. All 0 in this dataset

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

class dgl.data.FB15kDataset(reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

FB15k link prediction dataset.

Deprecated since version 0.5.0:

train is deprecated, it is replaced by:

>>> dataset = FB15kDataset()
>>> graph = dataset[0]
>>> train_mask = graph.edata['train_mask']
>>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(train_idx)
>>> rel = graph.edata['etype'][train_idx]

valid is deprecated, it is replaced by:

>>> dataset = FB15kDataset()
>>> graph = dataset[0]
>>> val_mask = graph.edata['val_mask']
>>> val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(val_idx)
>>> rel = graph.edata['etype'][val_idx]

test is deprecated, it is replaced by:

>>> dataset = FB15kDataset()
>>> graph = dataset[0]
>>> test_mask = graph.edata['test_mask']
>>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(test_idx)
>>> rel = graph.edata['etype'][test_idx]

The FB15K dataset was introduced in Translating Embeddings for Modeling Multi-relational Data. It is a subset of Freebase which contains about 14,951 entities with 1,345 different relations. When creating the dataset, a reverse edge with reversed relation types are created for each edge by default.

FB15k dataset statistics:

Nodes: 14,951
Number of relation types: 1,345
Number of reversed relation types: 1,345
Label Split:
- Train: 483142
- Valid: 50000
- Test: 59071

Parameters

reverse (bool) – Whether to add reverse edge. Default True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_nodes¶

Number of nodes

Type: int

num_rels¶

Number of relation types

Type: int

train¶

A numpy array of triplets (src, rel, dst) for the training graph

Type: numpy.ndarray

valid¶

A numpy array of triplets (src, rel, dst) for the validation graph

Type: numpy.ndarray

test¶

A numpy array of triplets (src, rel, dst) for the test graph

Type: numpy.ndarray

Examples

>>> dataset = FB15kDataset()
>>> g = dataset.graph
>>> e_type = g.edata['e_type']
>>>
>>> # get data split
>>> train_mask = g.edata['train_mask']
>>> val_mask = g.edata['val_mask']
>>>
>>> train_set = th.arange(g.number_of_edges())[train_mask]
>>> val_set = th.arange(g.number_of_edges())[val_mask]
>>>
>>> # build train_g
>>> train_edges = train_set
>>> train_g = g.edge_subgraph(train_edges,
                              relabel_nodes=False)
>>> train_g.edata['e_type'] = e_type[train_edges];
>>>
>>> # build val_g
>>> val_edges = th.cat([train_edges, val_edges])
>>> val_g = g.edge_subgraph(val_edges,
                            relabel_nodes=False)
>>> val_g.edata['e_type'] = e_type[val_edges];
>>>
>>> # Train, Validation and Test
>>>

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, FB15kDataset has only one graph object

Returns

The graph contains

edata['e_type']: edge relation type
edata['train_edge_mask']: positive training edge mask
edata['val_edge_mask']: positive validation edge mask
edata['test_edge_mask']: positive testing edge mask
edata['train_mask']: training edge set mask (include reversed training edges)
edata['val_mask']: validation edge set mask (include reversed validation edges)
edata['test_mask']: testing edge set mask (include reversed testing edges)
ndata['ntype']: node type. All 0 in this dataset

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

class dgl.data.WN18Dataset(reverse=True, raw_dir=None, force_reload=False, verbose=True)[source]¶

WN18 link prediction dataset.

Deprecated since version 0.5.0:

train is deprecated, it is replaced by:

>>> dataset = WN18Dataset()
>>> graph = dataset[0]
>>> train_mask = graph.edata['train_mask']
>>> train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(train_idx)
>>> rel = graph.edata['etype'][train_idx]

valid is deprecated, it is replaced by:

>>> dataset = WN18Dataset()
>>> graph = dataset[0]
>>> val_mask = graph.edata['val_mask']
>>> val_idx = th.nonzero(val_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(val_idx)
>>> rel = graph.edata['etype'][val_idx]

test is deprecated, it is replaced by:

>>> dataset = WN18Dataset()
>>> graph = dataset[0]
>>> test_mask = graph.edata['test_mask']
>>> test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
>>> src, dst = graph.edges(test_idx)
>>> rel = graph.edata['etype'][test_idx]

The WN18 dataset was introduced in Translating Embeddings for Modeling Multi-relational Data. It included the full 18 relations scraped from WordNet for roughly 41,000 synsets. When creating the dataset, a reverse edge with reversed relation types are created for each edge by default.

WN18 dataset statistics:

Nodes: 40943
Number of relation types: 18
Number of reversed relation types: 18
Label Split:
- Train: 141442
- Valid: 5000
- Test: 5000

Parameters

reverse (bool) – Whether to add reverse edge. Default True.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_nodes¶

Number of nodes

Type: int

num_rels¶

Number of relation types

Type: int

train¶

A numpy array of triplets (src, rel, dst) for the training graph

Type: numpy.ndarray

valid¶

A numpy array of triplets (src, rel, dst) for the validation graph

Type: numpy.ndarray

test¶

A numpy array of triplets (src, rel, dst) for the test graph

Type: numpy.ndarray

Examples

>>> dataset = WN18Dataset()
>>> g = dataset.graph
>>> e_type = g.edata['e_type']
>>>
>>> # get data split
>>> train_mask = g.edata['train_mask']
>>> val_mask = g.edata['val_mask']
>>>
>>> train_set = th.arange(g.number_of_edges())[train_mask]
>>> val_set = th.arange(g.number_of_edges())[val_mask]
>>>
>>> # build train_g
>>> train_edges = train_set
>>> train_g = g.edge_subgraph(train_edges,
                              relabel_nodes=False)
>>> train_g.edata['e_type'] = e_type[train_edges];
>>>
>>> # build val_g
>>> val_edges = th.cat([train_edges, val_edges])
>>> val_g = g.edge_subgraph(val_edges,
                            relabel_nodes=False)
>>> val_g.edata['e_type'] = e_type[val_edges];
>>>
>>> # Train, Validation and Test
>>>

__getitem__(idx)[source]¶

Gets the graph object

Parameters

idx (int) – Item index, WN18Dataset has only one graph object

Returns

The graph contains

edata['e_type']: edge relation type
edata['train_edge_mask']: positive training edge mask
edata['val_edge_mask']: positive validation edge mask
edata['test_edge_mask']: positive testing edge mask
edata['train_mask']: training edge set mask (include reversed training edges)
edata['val_mask']: validation edge set mask (include reversed validation edges)
edata['test_mask']: testing edge set mask (include reversed testing edges)
ndata['ntype']: node type. All 0 in this dataset

Return type

dgl.DGLGraph

__len__()[source]¶: The number of graphs in the dataset.

BitcoinOTC dataset¶

class dgl.data.BitcoinOTCDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

BitcoinOTC dataset for fraud detection

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users’ reputation to prevent transactions with fraudulent and risky users.

Offical website: https://snap.stanford.edu/data/soc-sign-bitcoin-otc.html

Bitcoin OTC dataset statistics:

Nodes: 5,881
Edges: 35,592
Range of edge weight: -10 to +10
Percentage of positive edges: 89%

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

graphs¶

A list of DGLGraph objects

Type: list

is_temporal¶

Indicate whether the graphs are temporal graphs

Type: bool

Raises: UserWarning – If the raw data is changed in the remote server by the author.

Examples

>>> dataset = BitcoinOTCDataset()
>>> len(dataset)
136
>>> for g in dataset:
....    # get edge feature
....    edge_weights = g.edata['h']
....    # your code here
>>>

__getitem__(item)[source]¶

Get graph by index

Parameters

item (int) – Item index

Returns

The graph contains:

edata['h'] : edge weights

Return type

dgl.DGLGraph

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

ICEWS18 dataset¶

class dgl.data.ICEWS18Dataset(mode='train', raw_dir=None, force_reload=False, verbose=False)[source]¶

ICEWS18 dataset for temporal graph

Integrated Crisis Early Warning System (ICEWS18)

Event data consists of coded interactions between socio-political actors (i.e., cooperative or hostile actions between individuals, groups, sectors and nation states). This Dataset consists of events from 1/1/2018 to 10/31/2018 (24 hours time granularity).

Reference:

Recurrent Event Network for Reasoning over Temporal Knowledge Graphs

ICEWS Coded Event Data

Statistics：

Train examples: 240
Valid examples: 30
Test examples: 34
Nodes per graph: 23033

Parameters

mode (str) – Load train/valid/test data. Has to be one of [‘train’, ‘valid’, ‘test’]
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

is_temporal¶

Is the dataset contains temporal graphs

Type: bool

Examples

>>> # get train, valid, test set
>>> train_data = ICEWS18Dataset()
>>> valid_data = ICEWS18Dataset(mode='valid')
>>> test_data = ICEWS18Dataset(mode='test')
>>>
>>> train_size = len(train_data)
>>> for g in train_data:
....    e_feat = g.edata['rel_type']
....    # your code here
....
>>>

__getitem__(idx)[source]¶

Get graph by index

Parameters

idx (int) – Item index

Returns

The graph contains:

edata['rel_type']: edge type

Return type

dgl.DGLGraph

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

GDELT dataset¶

class dgl.data.GDELTDataset(mode='train', raw_dir=None, force_reload=False, verbose=False)[source]¶

GDELT dataset for event-based temporal graph

The Global Database of Events, Language, and Tone (GDELT) dataset. This contains events happend all over the world (ie every protest held anywhere in Russia on a given day is collapsed to a single entry). This Dataset consists ofevents collected from 1/1/2018 to 1/31/2018 (15 minutes time granularity).

Reference:

Recurrent Event Network for Reasoning over Temporal Knowledge Graphs

The Global Database of Events, Language, and Tone (GDELT)

Statistics:

Train examples: 2,304
Valid examples: 288
Test examples: 384

Parameters

mode (str) – Must be one of (‘train’, ‘valid’, ‘test’). Default: ‘train’
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

start_time¶

Start time of the temporal graph

Type: int

end_time¶

End time of the temporal graph

Type: int

is_temporal¶

Does the dataset contain temporal graphs

Type: bool

Examples

>>> # get train, valid, test dataset
>>> train_data = GDELTDataset()
>>> valid_data = GDELTDataset(mode='valid')
>>> test_data = GDELTDataset(mode='test')
>>>
>>> # length of train set
>>> train_size = len(train_data)
>>>
>>> for g in train_data:
....    e_feat = g.edata['rel_type']
....    # your code here
....
>>>

__getitem__(t)[source]¶

Get graph by with events before time t + self.start_time

Parameters

t (int) – Time, its value must be in range [0, self.end_time - self.start_time]

Returns

The graph contains:

edata['rel_type']: edge type

Return type

dgl.DGLGraph

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

Graph Prediction Datasets¶

DGL hosted datasets for graph classification/regression tasks.

QM7b dataset¶

class dgl.data.QM7bDataset(raw_dir=None, force_reload=False, verbose=False)[source]¶

QM7b dataset for graph property prediction (regression)

This dataset consists of 7,211 molecules with 14 regression targets. Nodes means atoms and edges means bonds. Edge data ‘h’ means the entry of Coulomb matrix.

Reference: http://quantum-machine.org/datasets/

Statistics:

Number of graphs: 7,211
Number of regression targets: 14
Average number of nodes: 15
Average number of edges: 245
Edge feature size: 1

Parameters

raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_labels¶

Number of labels for each graph, i.e. number of prediction tasks

Type: int

Raises: UserWarning – If the raw data is changed in the remote server by the author.

Examples

>>> data = QM7bDataset()
>>> data.num_labels
14
>>>
>>> # iterate over the dataset
>>> for g, label in data:
...     edge_feat = g.edata['h']  # get edge feature
...     # your code here...
...
>>>

__getitem__(idx)[source]¶

Get graph and label by index

Parameters: idx (int) – Item index
Returns
Return type: (dgl.DGLGraph, Tensor)

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

QM9 dataset¶

class dgl.data.QM9Dataset(label_keys, cutoff=5.0, raw_dir=None, force_reload=False, verbose=False)[source]¶

QM9 dataset for graph property prediction (regression)

This dataset consists of 130,831 molecules with 12 regression targets. Nodes correspond to atoms and edges correspond to close atom pairs.

This dataset differs from QM9EdgeDataset in the following aspects:

Edges in this dataset are purely distance-based.
It only provides atoms’ coordinates and atomic numbers as node features
It only provides 12 regression targets.

Reference:

Statistics:

Number of graphs: 130,831
Number of regression targets: 12

Keys	Property	Description	Unit
mu	\(\mu\)	Dipole moment	\(\textrm{D}\)
alpha	\(\alpha\)	Isotropic polarizability	\({a_0}^3\)
homo	\(\epsilon_{\textrm{HOMO}}\)	Highest occupied molecular orbital energy	\(\textrm{eV}\)
lumo	\(\epsilon_{\textrm{LUMO}}\)	Lowest unoccupied molecular orbital energy	\(\textrm{eV}\)
gap	\(\Delta \epsilon\)	Gap between \(\epsilon_{\textrm{HOMO}}\) and \(\epsilon_{\textrm{LUMO}}\)	\(\textrm{eV}\)
r2	\(\langle R^2 \rangle\)	Electronic spatial extent	\({a_0}^2\)
zpve	\(\textrm{ZPVE}\)	Zero point vibrational energy	\(\textrm{eV}\)
U0	\(U_0\)	Internal energy at 0K	\(\textrm{eV}\)
U	\(U\)	Internal energy at 298.15K	\(\textrm{eV}\)
H	\(H\)	Enthalpy at 298.15K	\(\textrm{eV}\)
G	\(G\)	Free energy at 298.15K	\(\textrm{eV}\)
Cv	\(c_{\textrm{v}}\)	Heat capavity at 298.15K	\(\frac{\textrm{cal}}{\textrm{mol K}}\)

Parameters

label_keys (list) – Names of the regression property, which should be a subset of the keys in the table above.
cutoff (float) – Cutoff distance for interatomic interactions, i.e. two atoms are connected in the corresponding graph if the distance between them is no larger than this. Default: 5.0 Angstrom
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False
verbose (bool) – Whether to print out progress information. Default: True.

num_labels¶

Number of labels for each graph, i.e. number of prediction tasks

Type: int

Raises: UserWarning – If the raw data is changed in the remote server by the author.

Examples

>>> data = QM9Dataset(label_keys=['mu', 'gap'], cutoff=5.0)
>>> data.num_labels
2
>>>
>>> # iterate over the dataset
>>> for g, label in data:
...     R = g.ndata['R'] # get coordinates of each atom
...     Z = g.ndata['Z'] # get atomic numbers of each atom
...     # your code here...
>>>

__getitem__(idx)[source]¶

Get graph and label by index

Parameters

idx (int) – Item index

Returns

dgl.DGLGraph – The graph contains:
- ndata['R']: the coordinates of each atom
- ndata['Z']: the atomic number
Tensor – Property values of molecular graphs

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

QM9Edge dataset¶

class dgl.data.QM9EdgeDataset(label_keys=None, raw_dir=None, force_reload=False, verbose=True)[source]¶

QM9Edge dataset for graph property prediction (regression)

This dataset consists of 130,831 molecules with 19 regression targets. Nodes correspond to atoms and edges correspond to bonds.

This dataset differs from QM9Dataset in the following aspects:

It includes the bonds in a molecule in the edges of the corresponding graph while the edges in QM9Dataset are purely distance-based.
It provides edge features, and node features in addition to the atoms’ coordinates and atomic numbers.
It provides another 7 regression tasks(from 12 to 19).

This class is built based on a preprocessed version of the dataset, and we provide the preprocessing datails here.

Reference:

For Statistics:

Number of graphs: 130,831.
Number of regression targets: 19.

Node attributes:

pos: the 3D coordinates of each atom.
attr: the 11D atom features.

Edge attributes:

edge_attr: the 4D bond features.

Regression targets:

Keys	Property	Description	Unit
mu	\(\mu\)	Dipole moment	\(\textrm{D}\)
alpha	\(\alpha\)	Isotropic polarizability	\({a_0}^3\)
homo	\(\epsilon_{\textrm{HOMO}}\)	Highest occupied molecular orbital energy	\(\textrm{eV}\)
lumo	\(\epsilon_{\textrm{LUMO}}\)	Lowest unoccupied molecular orbital energy	\(\textrm{eV}\)
gap	\(\Delta \epsilon\)	Gap between \(\epsilon_{\textrm{HOMO}}\) and \(\epsilon_{\textrm{LUMO}}\)	\(\textrm{eV}\)
r2	\(\langle R^2 \rangle\)	Electronic spatial extent	\({a_0}^2\)
zpve	\(\textrm{ZPVE}\)	Zero point vibrational energy	\(\textrm{eV}\)
U0	\(U_0\)	Internal energy at 0K	\(\textrm{eV}\)
U	\(U\)	Internal energy at 298.15K	\(\textrm{eV}\)
H	\(H\)	Enthalpy at 298.15K	\(\textrm{eV}\)
G	\(G\)	Free energy at 298.15K	\(\textrm{eV}\)
Cv	\(c_{\textrm{v}}\)	Heat capavity at 298.15K	\(\frac{\textrm{cal}}{\textrm{mol K}}\)
U0_atom	\(U_0^{\textrm{ATOM}}\)	Atomization energy at 0K	\(\textrm{eV}\)
U_atom	\(U^{\textrm{ATOM}}\)	Atomization energy at 298.15K	\(\textrm{eV}\)
H_atom	\(H^{\textrm{ATOM}}\)	Atomization enthalpy at 298.15K	\(\textrm{eV}\)
G_atom	\(G^{\textrm{ATOM}}\)	Atomization free energy at 298.15K	\(\textrm{eV}\)
A	\(A\)	Rotational constant	\(\textrm{GHz}\)
B	\(B\)	Rotational constant	\(\textrm{GHz}\)
C	\(C\)	Rotational constant	\(\textrm{GHz}\)

Parameters

label_keys (list) – Names of the regression property, which should be a subset of the keys in the table above. If not provided, it will load all the labels.
raw_dir (str) – Raw file directory to download/contains the input data directory. Default: ~/.dgl/
force_reload (bool) – Whether to reload the dataset. Default: False.
verbose (bool) – Whether to print out progress information. Default: True.

num_labels¶

Number of labels for each graph, i.e. number of prediction tasks

Type: int

Raises: UserWarning – If the raw data is changed in the remote server by the author.

Examples

>>> data = QM9EdgeDataset(label_keys=['mu', 'alpha'])
>>> data.num_labels
2

>>> # iterate over the dataset
>>> for graph, labels in data:
...     print(graph) # get information of each graph
...     print(labels) # get labels of the corresponding graph
...     # your code here...
>>>

__getitem__(idx)[source]¶

Get graph and label by index

Parameters

idx (int) – Item index

Returns

dgl.DGLGraph – The graph contains:
- ndata['pos']: the coordinates of each atom
- ndata['attr']: the features of each atom
- edata['edge_attr']: the features of each bond
Tensor – Property values of molecular graphs

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

Mini graph classification dataset¶

class dgl.data.MiniGCDataset(num_graphs, min_num_v, max_num_v, seed=0, save_graph=True, force_reload=False, verbose=False)[source]¶

The synthetic graph classification dataset class.

The datset contains 8 different types of graphs.

class 0 : cycle graph
class 1 : star graph
class 2 : wheel graph
class 3 : lollipop graph
class 4 : hypercube graph
class 5 : grid graph
class 6 : clique graph
class 7 : circular ladder graph

Parameters

num_graphs (int) – Number of graphs in this dataset.
min_num_v (int) – Minimum number of nodes for graphs
max_num_v (int) – Maximum number of nodes for graphs
seed (int, default is 0) – Random seed for data generation

num_graphs¶

Number of graphs

Type: int

min_num_v¶

The minimum number of nodes

Type: int

max_num_v¶

The maximum number of nodes

Type: int

num_classes¶

The number of classes

Type: int

Examples

>>> data = MiniGCDataset(100, 16, 32, seed=0)

The dataset instance is an iterable

>>> len(data)
100
>>> g, label = data[64]
>>> g
Graph(num_nodes=20, num_edges=82,
      ndata_schemes={}
      edata_schemes={})
>>> label
tensor(5)

Batch the graphs and labels for mini-batch training

>>> graphs, labels = zip(*[data[i] for i in range(16)])
>>> batched_graphs = dgl.batch(graphs)
>>> batched_labels = torch.tensor(labels)
>>> batched_graphs
Graph(num_nodes=356, num_edges=1060,
      ndata_schemes={}
      edata_schemes={})

__getitem__(idx)[source]¶

Get the idx-th sample.

Parameters: idx (int) – The sample index.
Returns: The graph and its label.
Return type: (dgl.Graph, Tensor)

__len__()[source]¶: Return the number of graphs in the dataset.

TU dataset¶

class dgl.data.TUDataset(name, raw_dir=None, force_reload=False, verbose=False)[source]¶

TUDataset contains lots of graph kernel datasets for graph classification.

Parameters: name (str) – Dataset Name, such as ENZYMES, DD, COLLAB, MUTAG, can be the datasets name on https://chrsmrrs.github.io/datasets/docs/datasets/.

max_num_node¶

Maximum number of nodes

Type: int

num_labels¶

Number of classes

Type: int

Notes

IMPORTANT: Some of the datasets have duplicate edges exist in the graphs, e.g. the edges in IMDB-BINARY are all duplicated. DGL faithfully keeps the duplicates as per the original data. Other frameworks such as PyTorch Geometric removes the duplicates by default. You can remove the duplicate edges with dgl.to_simple().

Examples

>>> data = TUDataset('DD')

The dataset instance is an iterable

>>> len(data)
188
>>> g, label = data[1024]
>>> g
Graph(num_nodes=88, num_edges=410,
      ndata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64), 'node_labels': Scheme(shape=(1,), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})
>>> label
tensor([1])

Batch the graphs and labels for mini-batch training

>>> graphs, labels = zip(*[data[i] for i in range(16)])
>>> batched_graphs = dgl.batch(graphs)
>>> batched_labels = torch.tensor(labels)
>>> batched_graphs
Graph(num_nodes=9539, num_edges=47382,
      ndata_schemes={'node_labels': Scheme(shape=(1,), dtype=torch.int64), '_ID': Scheme(shape=(), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})

Notes

Graphs may have node labels, node attributes, edge labels, and edge attributes, varing from different dataset.

Labels are mapped to \(\lbrace 0,\cdots,n-1 \rbrace\) where \(n\) is the number of labels (some datasets have raw labels \(\lbrace -1, 1 \rbrace\) which will be mapped to \(\lbrace 0, 1 \rbrace\)). In previous versions, the minimum label was added so that \(\lbrace -1, 1 \rbrace\) was mapped to \(\lbrace 0, 2 \rbrace\).

__getitem__(idx)[source]¶

Get the idx-th sample.

Parameters: idx (int) – The sample index.
Returns: Graph with node feature stored in feat field and node label in node_label if available. And its label.
Return type: (dgl.DGLGraph, Tensor)

__len__()[source]¶: Return the number of graphs in the dataset.

class dgl.data.LegacyTUDataset(name, use_pandas=False, hidden_size=10, max_allow_node=None, raw_dir=None, force_reload=False, verbose=False)[source]¶

LegacyTUDataset contains lots of graph kernel datasets for graph classification.

Parameters

name (str) –
Dataset Name, such as ENZYMES, DD, COLLAB, MUTAG, can be the datasets name on https://chrsmrrs.github.io/datasets/docs/datasets/.
use_pandas (bool) – Numpy’s file read function has performance issue when file is large, using pandas can be faster. Default: False
hidden_size (int) – Some dataset doesn’t contain features. Use constant node features initialization instead, with hidden size as hidden_size. Default : 10
max_allow_node (int) – Remove graphs that contains more nodes than max_allow_node. Default : None

max_num_node¶

Maximum number of nodes

Type: int

num_labels¶

Number of classes

Type: int

Examples

>>> data = LegacyTUDataset('DD')

The dataset instance is an iterable

>>> len(data)
1178
>>> g, label = data[1024]
>>> g
Graph(num_nodes=88, num_edges=410,
      ndata_schemes={'feat': Scheme(shape=(89,), dtype=torch.float32), '_ID': Scheme(shape=(), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})
>>> label
tensor(1)

Batch the graphs and labels for mini-batch training

>>> graphs, labels = zip(*[data[i] for i in range(16)])
>>> batched_graphs = dgl.batch(graphs)
>>> batched_labels = torch.tensor(labels)
>>> batched_graphs
Graph(num_nodes=9539, num_edges=47382,
      ndata_schemes={'feat': Scheme(shape=(89,), dtype=torch.float32), '_ID': Scheme(shape=(), dtype=torch.int64)}
      edata_schemes={'_ID': Scheme(shape=(), dtype=torch.int64)})

Notes

LegacyTUDataset uses provided node feature by default. If no feature provided, it uses one-hot node label instead. If neither labels provided, it uses constant for node feature.

__getitem__(idx)[source]¶

Get the idx-th sample.

Parameters: idx (int) – The sample index.
Returns: Graph with node feature stored in feat field and node label in node_label if available. And its label.
Return type: (dgl.DGLGraph, Tensor)

__len__()[source]¶: Return the number of graphs in the dataset.

Graph isomorphism network dataset¶

class dgl.data.GINDataset(name, self_loop, degree_as_nlabel=False, raw_dir=None, force_reload=False, verbose=False)[source]¶

Dataset Class for How Powerful Are Graph Neural Networks?.

This is adapted from https://github.com/weihua916/powerful-gnns/blob/master/dataset.zip.

The class provides an interface for nine datasets used in the paper along with the paper-specific settings. The datasets are 'MUTAG', 'COLLAB', 'IMDBBINARY', 'IMDBMULTI', 'NCI1', 'PROTEINS', 'PTC', 'REDDITBINARY', 'REDDITMULTI5K'.

If degree_as_nlabel is set to False, then ndata['label'] stores the provided node label, otherwise ndata['label'] stores the node in-degrees.

For graphs that have node attributes, ndata['attr'] stores the node attributes. For graphs that have no attribute, ndata['attr'] stores the corresponding one-hot encoding of ndata['label'].

Parameters

name (str) – dataset name, one of ('MUTAG', 'COLLAB', 'IMDBBINARY', 'IMDBMULTI', 'NCI1', 'PROTEINS', 'PTC', 'REDDITBINARY', 'REDDITMULTI5K')
self_loop (bool) – add self to self edge if true
degree_as_nlabel (bool) – take node degree as label and feature if true

Examples

>>> data = GINDataset(name='MUTAG', self_loop=False)

The dataset instance is an iterable

>>> len(data)
188
>>> g, label = data[128]
>>> g
Graph(num_nodes=13, num_edges=26,
      ndata_schemes={'label': Scheme(shape=(), dtype=torch.int64), 'attr': Scheme(shape=(7,), dtype=torch.float32)}
      edata_schemes={})
>>> label
tensor(1)

Batch the graphs and labels for mini-batch training

>>> graphs, labels = zip(*[data[i] for i in range(16)])
>>> batched_graphs = dgl.batch(graphs)
>>> batched_labels = torch.tensor(labels)
>>> batched_graphs
Graph(num_nodes=330, num_edges=748,
      ndata_schemes={'label': Scheme(shape=(), dtype=torch.int64), 'attr': Scheme(shape=(7,), dtype=torch.float32)}
      edata_schemes={})

__getitem__(idx)[source]¶

Get the idx-th sample.

Parameters: idx (int) – The sample index.
Returns: The graph and its label.
Return type: (dgl.Graph, Tensor)

__len__()[source]¶: Return the number of graphs in the dataset.

Fake news dataset¶

class dgl.data.FakeNewsDataset(name, feature_name, raw_dir=None)[source]¶

Fake News Graph Classification dataset.

The dataset is composed of two sets of tree-structured fake/real news propagation graphs extracted from Twitter. Different from most of the benchmark datasets for the graph classification task, the graphs in this dataset are directed tree-structured graphs where the root node represents the news, the leaf nodes are Twitter users who retweeted the root news. Besides, the node features are encoded user historical tweets using different pretrained language models:

bert: the 768-dimensional node feature composed of Twitter user historical tweets encoded by the bert-as-service
content: the 310-dimensional node feature composed of a 300-dimensional “spacy” vector plus a 10-dimensional “profile” vector
profile: the 10-dimensional node feature composed of ten Twitter user profile attributes.
spacy: the 300-dimensional node feature composed of Twitter user historical tweets encoded by the spaCy word2vec encoder.

Reference: <https://github.com/safe-graph/GNN-FakeNews>

Note: this dataset is for academic use only, and commercial use is prohibited.

Statistics:

Politifact:

Graphs: 314

Nodes: 41,054

Edges: 40,740

Classes:

Fake: 157

Real: 157

Node feature size:

bert: 768

content: 310

profile: 10

spacy: 300

Gossipcop:

Graphs: 5,464

Nodes: 314,262

Edges: 308,798

Classes:

Fake: 2,732

Real: 2,732

Node feature size:

bert: 768

content: 310

profile: 10

spacy: 300

Parameters

name (str) – Name of the dataset (gossipcop, or politifact)
feature_name (str) – Name of the feature (bert, content, profile, or spacy)
raw_dir (str) – Specifying the directory that will store the downloaded data or the directory that already stores the input data. Default: ~/.dgl/

name¶

Name of the dataset (gossipcop, or politifact)

Type: str

num_classes¶

Number of label classes

Type: int

num_graphs¶

Number of graphs

Type: int

graphs¶

A list of DGLGraph objects

Type: list

labels¶

Graph labels

Type: Tensor

feature_name¶

Name of the feature (bert, content, profile, or spacy)

Type: str

feature¶

Node features

Type: Tensor

train_mask¶

Mask of training set

Type: Tensor

val_mask¶

Mask of validation set

Type: Tensor

test_mask¶

Mask of testing set

Type: Tensor

Examples

>>> dataset = FakeNewsDataset('gossipcop', 'bert')
>>> graph, label = dataset[0]
>>> num_classes = dataset.num_classes
>>> feat = dataset.feature
>>> labels = dataset.labels

__getitem__(i)[source]¶

Get graph and label by index

Parameters: i (int) – Item index
Returns
Return type: (dgl.DGLGraph, Tensor)

__len__()[source]¶

Number of graphs in the dataset.

Returns
Return type: int

Utilities¶

`utils.get_download_dir`()	Get the absolute path to the download directory.
`utils.download`(url[, path, overwrite, …])	Download a given URL.
`utils.check_sha1`(filename, sha1_hash)	Check whether the sha1 hash of the file content matches the expected hash.
`utils.extract_archive`(file, target_dir[, …])	Extract archive file.
`utils.split_dataset`(dataset[, frac_list, …])	Split dataset into training, validation and test set.
`utils.load_labels`(filename)	Load label dict from file
`utils.save_info`(path, info)	Save dataset related information into disk.
`utils.load_info`(path)	Load dataset related information from disk.

class dgl.data.utils.Subset(dataset, indices)[source]¶

Subset of a dataset at specified indices

Code adapted from PyTorch.

Parameters

dataset – dataset[i] should return the ith datapoint
indices (list) – List of datapoint indices to construct the subset

__getitem__(item)[source]¶

Get the datapoint indexed by item

Returns: datapoint
Return type: tuple

__len__()[source]¶

Get subset size

Returns: Number of datapoints in the subset
Return type: int