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GraphSage

A PyTorch implementation of the paper https://www-cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf

Requirements

Pytorch >=1.1.0

DGL: 0.4.3.post2

Results

The node classification results include two parts:

  • Full graph training on Cora citation dataset
  • Minibatch training on Reddit dataset

Full graph training

Run with following to train a GraphSage network on the Cora dataset:

python train_full_cora.py

Notice: This version not performs neighbor sampling (i.e. Algorithm 1 in the paper) so we feed the model with the entire graph and corresponding feature matrix.

  • GraphSage-Mean: ~ 80.4%
  • GraphSage-GCN: ~ 83.4%
  • GraphSage-Pool: ~ 72.5%

Minibatch training

DGL_MINIBATCH

Run with following to train a GraphSage network on the Reddit dataset:

python train_sampling_reddit.py

Notice: This version performs neighbor sampling in a layer-wise way (i.e. Algorithm 2 in the paper) so we feed the model with blocks (undirected bipartite graph).

  • GraphSage-Mean: ~ 96.33%
  • GraphSage-GCN: ~ 94.53%
  • GraphSage-Pool: ~ 88.35%

To-do:

  • LSTM aggregator

  • Minibatch training: Inductive graph splitting

  • Unsupervised training