Deep Graph Infomax (DGI) is a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual information between patch representations and corresponding high-level summaries of graphs---both derived using established graph convolutional network architectures.
The datasets contain three citation networks: CORA, PUBMED, CITESEER. The details for these three datasets can be found in the paper.
- paddlepaddle>=1.6
- pgl
We use DGI to pretrain embeddings for each nodes. Then we fix the embedding to train a node classifier.
| Dataset | Accuracy |
|---|---|
| Cora | ~81% |
| Pubmed | ~77.6% |
| Citeseer | ~71.3% |
For examples, use gpu to train gcn on cora dataset.
python dgi.py --dataset cora --use_cuda
python train.py --dataset cora --use_cuda
- dataset: The citation dataset "cora", "citeseer", "pubmed".
- use_cuda: Use gpu if assign use_cuda.