gcn

Graph Convolutional Networks (GCN)

Paper link: https://arxiv.org/abs/1609.02907 Author's code repo: https://github.com/tkipf/gcn

Dependencies

• MXNet nightly build
• requests

bash pip install mxnet --pre pip install requests

Codes

The folder contains three implementations of GCN:

• gcn.py uses DGL's predefined graph convolution module.
• gcn_mp.py uses user-defined message and reduce functions. Modify train.py to switch between different implementations.

The provided implementation in gcn_concat.py is a bit different from the original paper for better performance, credit to @yifeim and @ZiyueHuang.

Results

Run with following (available dataset: "cora", "citeseer", "pubmed")

DGLBACKEND=mxnet python3 train.py --dataset cora --gpu 0 --self-loop

• cora: ~0.810 (paper: 0.815)
• citeseer: ~0.702 (paper: 0.703)
• pubmed: ~0.780 (paper: 0.790)

Results (gcn_concat.py vs. gcn.py)

gcn_concat.py uses concatenation of hidden units to account for multi-hop skip-connections. We feel concatenation is superior because all neighboring information is presented without additional modeling assumptions. These results are based on single-run training to minimize the cross-entropy loss. We can see clear skip connection can help train a GCN with many layers.

The experiments show that adding depth may or may not improve accuracy. While adding depth is a clear way to mimic power iterations of matrix factorizations, training multiple epochs to obtain stationary points could equivalently solve matrix factorization. Given the small datasets, we can't draw such conclusions from these experiments.

# Final accuracy 57.70% MLP without GCN
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "citeseer" --n-epochs 200 --n-layers 0

# Final accuracy 65.70% with 10-layer GCN with skip connection
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "citeseer" --n-epochs 200 --n-layers 2 --normalization 'sym' --self-loop

# Final accuracy 64.70% with 10-layer GCN with skip connection
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "citeseer" --n-epochs 200 --n-layers 10 --normalization 'sym' --self-loop

# Final accuracy 53.20% MLP without GCN
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "cora" --n-epochs 200 --n-layers 0

# Final accuracy 72.60% with 2-layer GCN with skip connection
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "cora" --n-epochs 200 --n-layers 2 --normalization 'sym' --self-loop

# Final accuracy 78.90% with 10-layer GCN with skip connection
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "cora" --n-epochs 200 --n-layers 10 --normalization 'sym' --self-loop

# Final accuracy 70.30% MLP without GCN
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "pubmed" --n-epochs 200 --n-layers 0

# Final accuracy 78.30% with 2-layer GCN with skip connection
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "pubmed" --n-epochs 200 --n-layers 2 --normalization 'sym' --self-loop

# Final accuracy 76.30% with 10-layer GCN with skip connection
DGLBACKEND=mxnet python3 examples/mxnet/gcn/gcn_concat.py --dataset "pubmed" --n-epochs 200 --n-layers 10 --normalization 'sym' --self-loop