dgl.transforms

Transform for structures and features

BaseTransform	An abstract class for writing transforms.
Compose	Create a transform composed of multiple transforms in sequence.
AddSelfLoop	Add self-loops for each node in the graph and return a new graph.
RemoveSelfLoop	Remove self-loops for each node in the graph and return a new graph.
AddReverse	Add a reverse edge \((i,j)\) for each edge \((j,i)\) in the input graph and return a new graph.
ToSimple	Convert a graph to a simple graph without parallel edges and return a new graph.
LineGraph	Return the line graph of the input graph.
KHopGraph	Return the graph whose edges connect the \(k\)-hop neighbors of the original graph.
AddMetaPaths	Add new edges to an input graph based on given metapaths, as described in Heterogeneous Graph Attention Network.
GCNNorm	Apply symmetric adjacency normalization to an input graph and save the result edge weights, as described in Semi-Supervised Classification with Graph Convolutional Networks.
PPR	Apply personalized PageRank (PPR) to an input graph for diffusion, as introduced in The pagerank citation ranking: Bringing order to the web.
HeatKernel	Apply heat kernel to an input graph for diffusion, as introduced in Diffusion kernels on graphs and other discrete structures.
GDC	Apply graph diffusion convolution (GDC) to an input graph, as introduced in Diffusion Improves Graph Learning.
NodeShuffle	Randomly shuffle the nodes.
DropNode	Randomly drop nodes, as described in Graph Contrastive Learning with Augmentations.
DropEdge	Randomly drop edges, as described in DropEdge: Towards Deep Graph Convolutional Networks on Node Classification and Graph Contrastive Learning with Augmentations.
AddEdge	Randomly add edges, as described in Graph Contrastive Learning with Augmentations.
RandomWalkPE	Random Walk Positional Encoding, as introduced in Graph Neural Networks with Learnable Structural and Positional Representations
LaplacianPE	Laplacian Positional Encoding, as introduced in Benchmarking Graph Neural Networks
FeatMask	Randomly mask columns of the node and edge feature tensors, as described in Graph Contrastive Learning with Augmentations.
RowFeatNormalizer	Row-normalizes the features given in node_feat_names and edge_feat_names.
SIGNDiffusion	The diffusion operator from SIGN: Scalable Inception Graph Neural Networks
ToLevi	This function transforms the original graph to its heterogeneous Levi graph, by converting edges to intermediate nodes, only support homogeneous directed graph.