{"id":13678340,"url":"https://github.com/caojiangxia/BiGI","last_synced_at":"2025-04-29T13:30:31.008Z","repository":{"id":112472411,"uuid":"305311343","full_name":"caojiangxia/BiGI","owner":"caojiangxia","description":"[WSDM 2021]Bipartite Graph Embedding via Mutual Information Maximization","archived":false,"fork":false,"pushed_at":"2021-07-06T09:49:17.000Z","size":1980,"stargazers_count":74,"open_issues_count":0,"forks_count":13,"subscribers_count":5,"default_branch":"main","last_synced_at":"2024-08-02T13:21:56.327Z","etag":null,"topics":["bipartite-graphs","deep-infomax","graph-embedding","graph-neural-networks","recommender-system","self-supervised-learning"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2012.05442","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/caojiangxia.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2020-10-19T08:16:52.000Z","updated_at":"2024-07-09T12:36:42.000Z","dependencies_parsed_at":"2023-05-15T08:00:13.566Z","dependency_job_id":null,"html_url":"https://github.com/caojiangxia/BiGI","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caojiangxia%2FBiGI","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caojiangxia%2FBiGI/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caojiangxia%2FBiGI/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caojiangxia%2FBiGI/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/caojiangxia","download_url":"https://codeload.github.com/caojiangxia/BiGI/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224173783,"owners_count":17268177,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bipartite-graphs","deep-infomax","graph-embedding","graph-neural-networks","recommender-system","self-supervised-learning"],"created_at":"2024-08-02T13:00:52.549Z","updated_at":"2024-11-11T20:31:25.609Z","avatar_url":"https://github.com/caojiangxia.png","language":"Python","readme":"BiGI\n===\n\nThe source code is for the paper: ”Bipartite Graph Embedding via Mutual Information Maximization\" accepted in WSDM 2021 by Jiangxia Cao*, Xixun Lin*, Shu Guo, Luchen Liu, Tingwen Liu, Bin Wang (* means equal contribution).\n\n\n```\n@inproceedings{bigi2021,\n  title={Bipartite Graph Embedding via Mutual Information Maximization},\n  author={Cao*, Jiangxia and Lin*, Xixun and Guo, Shu and Liu, Luchen and Liu, Tingwen and Wang, Bin},\n  booktitle={ACM International Conference on Web Search and Data Mining (WSDM)},\n  year={2021}\n}\n```\n\n\nRequirements\n---\n\nPython=3.6.2\n\nPyTorch=1.1.0\n\nCUDA=9.0\n\nScikit-Learn = 0.22\n\nScipy = 1.3.1\n\nPreparation\n---\n\nSome datasets have been included in the `./dataset` directory. Other datasets can be downloaded from the [official website](https://grouplens.org/datasets/movielens/).\n\nUsage\n---\n\nTo run this project, please make sure that you have the following packages being downloaded. Our experiments are conducted on a PC with an Intel Xeon E5 2.1GHz CPU and a Tesla V100 GPU.\n\nFor running DBLP:\n\n```shell\nCUDA_VISIBLE_DEVICES=1 nohup python -u train_rec.py --id dblp --struct_rate 0.00001 --GNN 2 \u003e BiGIdblp.log 2\u003e\u00261\u0026\n```\n\nFor running ML-100K:\n\n```shell\nCUDA_VISIBLE_DEVICES=1 nohup python -u train_rec.py --data_dir dataset/movie/ml-100k/1/ --batch_size 128 --id ml100k --struct_rate 0.0001 --GNN 2 \u003e BiGI100k.log 2\u003e\u00261\u0026\n```\n\nFor running ML-10M:\n\n```shell\nCUDA_VISIBLE_DEVICES=1 nohup python -u train_rec.py --batch_size 100000 --data_dir dataset/movie/ml-10m/ml-10M100K/1/ --id ml10m --struct_rate 0.00001 \u003e BiGI10m.log 2\u003e\u00261\u0026\n```\n\nFor running Wiki(5:5):\n\n```shell\nCUDA_VISIBLE_DEVICES=1 nohup python -u train_lp.py --id wiki5 --struct_rate 0.0001 --GNN 2 \u003e BiGIwiki5.log 2\u003e\u00261\u0026\n```\n\nFor running Wiki(4:6):\n\n```shell\nCUDA_VISIBLE_DEVICES=1 nohup python -u train_lp.py --data_dir dataset/wiki/4/ --id wiki4 --struct_rate 0.0001 --GNN 2 \u003e BiGIwiki4.log 2\u003e\u00261\u0026\n```\n\n","funding_links":[],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaojiangxia%2FBiGI","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcaojiangxia%2FBiGI","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaojiangxia%2FBiGI/lists"}