# DGL Basics¶

Author: Minjie Wang, Quan Gan, Yu Gai, Zheng Zhang

The Goal of this tutorial:

• To create a graph.
• To read and write node and edge representations.

## Graph Creation¶

The design of DGLGraph was influenced by other graph libraries. Indeed, you can create a graph from networkx, and convert it into a DGLGraph and vice versa:

import networkx as nx
import dgl

g_nx = nx.petersen_graph()
g_dgl = dgl.DGLGraph(g_nx)

import matplotlib.pyplot as plt
plt.subplot(121)
nx.draw(g_nx, with_labels=True)
plt.subplot(122)
nx.draw(g_dgl.to_networkx(), with_labels=True)

plt.show() They are the same graph, except that DGLGraph is always directional.

One can also create a graph by calling DGL’s own interface.

Now let’s build a star graph. DGLGraph nodes are consecutive range of integers between 0 and number_of_nodes() and can grow by calling add_nodes. DGLGraph edges are in order of their additions. Note that edges are accessed in much the same way as nodes, with one extra feature of edge broadcasting:

import dgl
import torch as th

g = dgl.DGLGraph()
# a couple edges one-by-one
for i in range(1, 4):
# a few more with a paired list
src = list(range(5, 8)); dst = *3
# finish with a pair of tensors
src = th.tensor([8, 9]); dst = th.tensor([0, 0])

# edge broadcasting will do star graph in one go!
src = th.tensor(list(range(1, 10)));

import networkx as nx
import matplotlib.pyplot as plt
nx.draw(g.to_networkx(), with_labels=True)
plt.show() ## Feature Assignment¶

One can also assign features to nodes and edges of a DGLGraph. The features are represented as dictionary of names (strings) and tensors, called fields.

The following code snippet assigns each node a vector (len=3).

Note

DGL aims to be framework-agnostic, and currently it supports PyTorch and MXNet tensors. From now on, we use PyTorch as an example.

import dgl
import torch as th

x = th.randn(10, 3)
g.ndata['x'] = x


ndata is a syntax sugar to access states of all nodes, states are stored in a container data that hosts user defined dictionary.

print(g.ndata['x'] == g.nodes[:].data['x'])

# access node set with integer, list, or integer tensor
g.nodes.data['x'] = th.zeros(1, 3)
g.nodes[[0, 1, 2]].data['x'] = th.zeros(3, 3)
g.nodes[th.tensor([0, 1, 2])].data['x'] = th.zeros(3, 3)


Out:

tensor([[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]], dtype=torch.uint8)


Assigning edge features is in a similar fashion to that of node features, except that one can also do it by specifying endpoints of the edges.

g.edata['w'] = th.randn(9, 2)

# access edge set with IDs in integer, list, or integer tensor
g.edges.data['w'] = th.randn(1, 2)
g.edges[[0, 1, 2]].data['w'] = th.zeros(3, 2)
g.edges[th.tensor([0, 1, 2])].data['w'] = th.zeros(3, 2)

# one can also access the edges by giving endpoints
g.edges[1, 0].data['w'] = th.ones(1, 2)                 # edge 1 -> 0
g.edges[[1, 2, 3], [0, 0, 0]].data['w'] = th.ones(3, 2) # edges [1, 2, 3] -> 0


After assignments, each node/edge field will be associated with a scheme containing the shape and data type (dtype) of its field value.

print(g.node_attr_schemes())
g.ndata['x'] = th.zeros((10, 4))
print(g.node_attr_schemes())


Out:

{'x': Scheme(shape=(3,), dtype=torch.float32)}
{'x': Scheme(shape=(4,), dtype=torch.float32)}


One can also remove node/edge states from the graph. This is particularly useful to save memory during inference.

g.ndata.pop('x')
g.edata.pop('w')


### Multigraphs¶

Many graph applications need multi-edges. To enable this, construct DGLGraph with multigraph=True.

g_multi = dgl.DGLGraph(multigraph=True)
g_multi.ndata['x'] = th.randn(10, 2)

g_multi.add_edge(1, 0) # two edges on 1->0

g_multi.edata['w'] = th.randn(10, 2)
g_multi.edges.data['w'] = th.zeros(1, 2)
print(g_multi.edges())


Out:

(tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 1]), tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]))


An edge in multi-graph cannot be uniquely identified using its incident nodes $$u$$ and $$v$$; query their edge ids use edge_id interface.

eid_10 = g_multi.edge_id(1, 0)
g_multi.edges[eid_10].data['w'] = th.ones(len(eid_10), 2)
print(g_multi.edata['w'])


Out:

tensor([[ 1.0000,  1.0000],
[ 0.0000,  0.0000],
[-0.2588,  0.3623],
[ 2.8029,  0.5967],
[-1.7736,  1.1910],
[ 0.3487,  0.6900],
[-1.3407, -0.8081],
[-2.0258,  0.1400],
[ 2.6088, -1.0804],
[ 1.0000,  1.0000]])


Note

• Nodes and edges can be added but not removed; we will support removal in the future.
• Updating a feature of different schemes raise error on individual node (or node subset).

## Next steps¶

In the next tutorial, we will go through the DGL message passing interface by implementing PageRank.

Total running time of the script: ( 0 minutes 0.071 seconds)

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