Models of Cogdl
Introduction to graph representation learning
Inspired by recent trends of representation learning on computer vision and natural language processing, graph representation learning is proposed as an efficient technique to address this issue. Graph representation aims at either learning low-dimensional continuous vectors for vertices/graphs while preserving intrinsic graph properties, or using graph encoders to an end-to-end training.
Recently, graph neural networks (GNNs) have been proposed and have achieved impressive performance in semi-supervised representation learning. Graph Convolution Networks (GCNs) proposes a convolutional architecture via a localized first-order approximation of spectral graph convolutions. GraphSAGE is a general inductive framework that leverages node features to generate node embeddings for previously unseen samples. Graph Attention Networks (GATs) utilizes the multi-head self-attention mechanism and enables (implicitly) specifying different weights to different nodes in a neighborhood.
CogDL now supports the following tasks
unsupervised node classification
semi-supervised node classification
heterogeneous node classification
link prediction
multiplex link prediction
unsupervised graph classification
supervised graph classification
graph pre-training
attributed graph clustering
CogDL provides abundant of common benchmark datasets and GNN models. You can simply start a running using models and datasets in CogDL.
from cogdl import experiment
experiment(model="gcn", dataset="cora")
Unsupervised Multi-label Node Classification
Model |
Name in Cogdl |
---|---|
NetMF (Qiu et al, WSDM’18) |
netmf |
ProNE (Zhang et al, IJCAI’19) |
prone |
NetSMF (Qiu et at, WWW’19) |
netsmf |
Node2vec (Grover et al, KDD’16) |
node2vec |
LINE (Tang et al, WWW’15) |
line |
DeepWalk (Perozzi et al, KDD’14) |
deepwalk |
spectral |
|
Hope (Ou et al, KDD’16) |
hope |
GraRep (Cao et al, CIKM’15) |
grarep |
Semi-Supervised Node Classification with Attributes
Model |
Name in Cogdl |
---|---|
Grand(Feng et al.,NLPS’20) |
grand |
GCNII(Chen et al.,ICML’20) |
gcnii |
DR-GAT (Zou et al., 2019) |
drgat |
MVGRL (Hassani et al., KDD’20) |
mvgrl |
ppnp |
|
gat |
|
GDC_GCN (Klicpera et al., NeurIPS’19) |
gdc_gcn |
DropEdge (Rong et al., ICLR’20) |
dropedge_gcn |
gcn |
|
dgi |
|
GraphSAGE (Hamilton et al., NeurIPS’17) |
graphsage |
GraphSAGE (unsup)(Hamilton et al., NeurIPS’17) |
unsup_graphsage |
mixhop |
Multiplex Node Classification
Model |
Name in Cogdl |
---|---|
Simple-HGN (Lv and Ding et al, KDD’21) |
|
gtn |
|
han |
|
gcc |
|
pte |
|
Metapath2vec (Dong et al, KDD’17) |
metapath2vec |
Hin2vec (Fu et al, CIKM’17) |
hin2vec |
Link Prediction
Model |
Name in Cogdl |
---|---|
ProNE (Zhang et al, IJCAI’19) |
prone |
NetMF (Qiu et al, WSDM’18) |
netmf |
Hope (Ou et al, KDD’16) |
hope |
LINE (Tang et al, WWW’15) |
line |
Node2vec (Grover et al, KDD’16) |
node2vec |
NetSMF (Qiu et at, WWW’19) |
netsmf |
DeepWalk (Perozzi et al, KDD’14) |
deepwalk |
SDNE (Wang et al, KDD’16) |
sdne |
Multiplex Link Prediction
Model |
Name in Cogdl |
---|---|
GATNE (Cen et al, KDD’19) |
gatne |
NetMF (Qiu et al, WSDM’18) |
netmf |
ProNE (Zhang et al, IJCAI’19) |
prone++ |
Node2vec (Grover et al, KDD’16) |
node2vec |
DeepWalk (Perozzi et al, KDD’14) |
deepwalk |
LINE (Tang et al, WWW’15) |
line |
Hope (Ou et al, KDD’16) |
hope |
GraRep (Cao et al, CIKM’15) |
grarep |
Knowledge graph completion
Model |
Name in Cogdl |
---|---|
CompGCN (Vashishth et al, ICLR’20) |
compgcn |
Graph Classification
Model |
Name in Cogdl |
---|---|
gin |
|
Infograph (Sun et al, ICLR’20) |
infograph |
DiffPool (Ying et al, NeuIPS’18) |
diffpool |
SortPool (Zhang et al, AAAI’18) |
softpool |
Graph2Vec (Narayanan et al, CoRR’17) |
graph2vec |
PATCH_SAN (Niepert et al, ICML’16) |
patchy_san |
dgk |
Attributed graph clustering
Model |
Name in Cogdl |
---|---|
agc |
|
DAEGC (Wang et al, ICLR’20) |
daegc |