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Adversarial Representation Learning on Large-Scale Bipartite Graphs

Reproducibility

Preparation

pip3 install -r requirements.txt

Peproduciable Scripts Overview

ABCGraph (Adversarial) ABCGraph (MLP) Node2Vec GCN GraphSAGE GAE
Platform MacOS/Linux MacOS/Linux Only Linux (*) MacOS/Linux MacOS/Linux MacOS/Linux
Tencent sh run_abcgraph_adv.sh tencent sh run_abcgraph_mlp.sh tencent sh run_node2vec.sh tencent sh run_gcn.sh tencent sh run_graphsage.sh tencent N/A (*)
Cora sh run_abcgraph_adv.sh cora sh run_abcgraph_mlp.sh cora sh run_node2vec.sh cora sh run_gcn.sh cora sh run_graphsage.sh cora sh run_gae.sh cora
Citeseer sh run_abcgraph_adv.sh citeseer sh run_abcgraph_mlp.sh citeseer sh run_node2vec.sh citeseer sh run_gcn.sh citeseer sh run_graphsage.sh citeseer sh run_gae.sh citeseer
PubMed sh run_abcgraph_adv.sh pubmed sh run_abcgraph_mlp.sh pubmed sh run_node2vec.sh pubmed sh run_gcn.sh pubmed sh run_graphsage.sh pubmed sh run_gae.sh pubmed

Only Linux (*): For the Node2Vec model, its binary file is only ELF 64-bit LSB executable, x86-64, for GNU/Linux.

N/A (*): For the GAE model, the code of the original GAE paper can not simply applied to the large-scale bipartite graph due to the memory constrain. To apply GAE to the large-scale graph data is another research topic, so we don't report the result in the "Tencent" dataset. From the other datasets, we can see that our model's performance is better than the GAE model.

Background running:

# ABCGraph (Adversarial)
nohup sh run_abcgraph_adv.sh tencent > log_run_abcgraph_adv_tencent.txt 2>&1 &
nohup sh run_abcgraph_adv.sh cora > log_run_abcgraph_adv_cora.txt 2>&1 &
nohup sh run_abcgraph_adv.sh citeseer > log_run_abcgraph_adv_citeseer.txt 2>&1 &
nohup sh run_abcgraph_adv.sh pubmed > log_run_abcgraph_adv_pubmed.txt 2>&1 &

# ABCGraph (MLP)
nohup sh run_abcgraph_mlp.sh tencent > log_run_abcgraph_mlp_tencent.txt 2>&1 &
nohup sh run_abcgraph_mlp.sh cora > log_run_abcgraph_mlp_cora.txt 2>&1 &
nohup sh run_abcgraph_mlp.sh citeseer > log_run_abcgraph_mlp_citeseer.txt 2>&1 &
nohup sh run_abcgraph_mlp.sh pubmed > log_run_abcgraph_mlp_pubmed.txt 2>&1 &

# Node2Vec
nohup sh run_node2vec.sh tencent > log_run_node2vec_tencent.txt 2>&1 &
nohup sh run_node2vec.sh cora > log_run_node2vec_cora.txt 2>&1 &
nohup sh run_node2vec.sh citeseer > log_run_node2vec_citeseer.txt 2>&1 &
nohup sh run_node2vec.sh pubmed > log_run_node2vec_pubmed.txt 2>&1 &


# GCN
nohup sh run_gcn.sh tencent > log_run_gcn_tencent.txt 2>&1 &
nohup sh run_gcn.sh cora > log_run_gcn_cora.txt 2>&1 &
nohup sh run_gcn.sh citeseer > log_run_gcn_citeseer.txt 2>&1 &
nohup sh run_gcn.sh pubmed > log_run_gcn_pubmed.txt 2>&1 &

# GraphSAGE
nohup sh run_graphsage.sh tencent > log_run_graphsage_tencent.txt 2>&1 &
nohup sh run_graphsage.sh cora > log_run_graphsage_cora.txt 2>&1 &
nohup sh run_graphsage.sh citeseer > log_run_graphsage_citeseer.txt 2>&1 &
nohup sh run_graphsage.sh pubmed > log_run_graphsage_pubmed.txt 2>&1 &

# GAE
nohup sh run_gae.sh tencent > log_run_gae_tencent.txt 2>&1 &
nohup sh run_gae.sh cora > log_run_gae_cora.txt 2>&1 &
nohup sh run_gae.sh citeseer > log_run_gae_citeseer.txt 2>&1 &
nohup sh run_gae.sh pubmed > log_run_gae_pubmed.txt 2>&1 &

# AS-GCN
cd ASGCN/data
python tencent_dataset_loader.py
nohup python run_pubmed.py --dataset tencent > running_asgcn.txt

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