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README.md
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graphgan_trainer.py
graphgan_trainer.py
 
 

README.md

Graph Generative Adversarial Nets (GraphGAN)

Dataset Statics

Dataset # Nodes # Edges
arXiv-GrQc 5242 14496

Files Description

  • gan_datasets : folds to save training data

    • CA-GrQc_train : training data
    • CA-GrQc_test : positive sampling of test data
    • CA-GrQc_test_neg : negative sampling of test data

    The data should be an undirected graph in which node IDs start from 0 to N-1 (N is the number of nodes in the graph). Each line contains two node IDs indicating an edge in the graph.

    txt file sample : 0 1 3 2 ...

    • CA-GrQc_pre_train.emb : pre-trained node embeddings

    Note: the dimension of pre-trained node embeddings should equal parameter 'n_emb'

  • gan_cache

    • CA-GrQc.pkl : save constructed BFS-trees

  • gan_results

    • CA-GrQc.txt : evaluation results
    • CA-GrQc_best_acc_gen_.emb : embeddings of generator with the best evaluation
    • CA-GrQc_best_acc_dis_.emb : embeddings of discriminator with the best evaluation
    • CA-GrQc_gen_.emb : learned embeddings of generator during training
    • CA-GrQc_dis_.emb : learned embeddings of discriminator during training

  • checkpoint : folds to save the model weights

Results

TL_BACKEND="paddle" python graphgan_trainer.py --batch_size_dis 1024 --batch_size_gen 1024 --n_epochs 30 --lr_gen 1e-5 --lr_dis 1e-5
TL_BACKEND="tensorflow" python graphgan_trainer.py --batch_size_dis 1024 --batch_size_gen 1024 --n_epochs 30 --lr_gen 1e-5 --lr_dis 1e-5
TL_BACKEND="torch" python graphgan_trainer.py --batch_size_dis 1024 --batch_size_gen 1024 --n_epochs 30 --lr_gen 1e-5 --lr_dis 1e-5
Dataset Paper Our(pd) Our(tf) Our(torch)
arXiv-GrQc 0.849 0.8813±0.00069 0.8819±0.00133 0.8820