dmlc--dgl
彭齐荣Inductive Representation Learning on Large Graphs (GraphSAGE)
- Paper link: http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf
- Author's code repo: https://github.com/williamleif/graphsage-simple. Note that the original code is simple reference implementation of GraphSAGE.
Advanced usages, including how to run pure GPU sampling, how to train with PyTorch Lightning, etc., are in the advanced directory.
Requirements
pip install requests torchmetrics
Results
Full graph training
Run with following (available dataset: "cora", "citeseer", "pubmed")
python3 train_full.py --dataset cora --gpu 0 # full graph
- cora: ~0.8330
- citeseer: ~0.7110
- pubmed: ~0.7830
Minibatch training for node classification
Train w/ mini-batch sampling for node classification on OGB-products:
python3 node_classification.py
python3 multi_gpu_node_classification.py
PyTorch Lightning for node classification
Train w/ mini-batch sampling for node classification with PyTorch Lightning on OGB-products. Works with both single GPU and multiple GPUs:
python3 lightning/node_classification.py
Minibatch training for link prediction
Train w/ mini-batch sampling for link prediction on OGB-Citation2:
python3 link_pred.py