GAlign

Code of the paper: Adaptive Network Alignment with Multi-order Convolutional Networks.

Environment

• python>=3.5
• networkx >= 2.4
• pytorch >= 1.2.0
• numpy >= 1.18.1

Running

For allmv_tmdb dataset

python -u network_alignment.py --source_dataset graph_data/allmv_tmdb/allmv/graphsage --target_dataset graph_data/allmv_tmdb/tmdb/graphsage --groundtruth graph_data/allmv_tmdb/dictionaries/groundtruth GAlign --log --GAlign_epochs 10 --refinement_epochs 50 --cuda

For douban dataset

python -u network_alignment.py --source_dataset graph_data/douban/online/graphsage --target_dataset graph_data/douban/offline/graphsage --groundtruth graph_data/douban/dictionaries/groundtruth GAlign --log --GAlign_epochs 50 --refinement_epochs 0 --cuda --embedding_dim 128 --lr 0.005 --noise_level 0 --refinement_epochs 0 

Citation

Please politely cite our work as follows:

Huynh Thanh Trung, Tong Van Vinh, Nguyen Thanh Tam, Hongzhi Yin, Matthias Weidlich, Nguyen Quoc Viet Hung. Adaptive Network Alignment with Multi-order Convolutional Networks. In: ICDE 2020