{"id":13788780,"url":"https://github.com/limaosen0/Variational-Graph-Auto-Encoders","last_synced_at":"2025-05-12T03:30:42.078Z","repository":{"id":213907526,"uuid":"133779210","full_name":"limaosen0/Variational-Graph-Auto-Encoders","owner":"limaosen0","description":"This is the implementation of paper 'Variational Graph Auto-Encoder' in NIPS Workshop on Bayesian Deep Learning, 2016.","archived":false,"fork":false,"pushed_at":"2018-05-20T13:58:25.000Z","size":322,"stargazers_count":74,"open_issues_count":2,"forks_count":23,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-11-18T02:38:53.945Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/limaosen0.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2018-05-17T07:56:32.000Z","updated_at":"2024-07-20T03:42:38.000Z","dependencies_parsed_at":null,"dependency_job_id":"6f58b0c0-ccb9-4f5c-a6cd-d419ed3391bb","html_url":"https://github.com/limaosen0/Variational-Graph-Auto-Encoders","commit_stats":null,"previous_names":["limaosen0/variational-graph-auto-encoders"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/limaosen0%2FVariational-Graph-Auto-Encoders","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/limaosen0%2FVariational-Graph-Auto-Encoders/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/limaosen0%2FVariational-Graph-Auto-Encoders/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/limaosen0%2FVariational-Graph-Auto-Encoders/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/limaosen0","download_url":"https://codeload.github.com/limaosen0/Variational-Graph-Auto-Encoders/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253667942,"owners_count":21944943,"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":[],"created_at":"2024-08-03T21:00:53.395Z","updated_at":"2025-05-12T03:30:41.617Z","avatar_url":"https://github.com/limaosen0.png","language":"Python","funding_links":[],"categories":["TensorFlow Implementations","其他_图神经网络GNN"],"sub_categories":["网络服务_其他"],"readme":"# Variational-Graph-Auto-Encoders\nThis is the implementation of paper \"Variational Graph Auto-Encoders\", which is published in NIPS 2016 Workshop.\n\nThomas N. Kipf, Max Welling, Variational Graph Auto-Encoders, In NIPS Workshop on Bayesian Deep Learning, 2016.\n\n# How to run the code?\nInsure that you have 4 GB memory in your GPU and you have installed the required module.\n\nWe have provided the training data in right form in './data', and the main program 'main.py' can be run directly, like input 'CUDA_VISIBLE_DEVICES=0 python main.py' in terminal. For the first time of running, the program will load the data and generate a graph, which might cost much time. After that, a loaded graph would be saved in a numpy file in './data', and you can load the graph more efficiently.\n\nDuring the training process, the program would print the loss value and validation accuracy. At last, the ROC curve would be saved in './result'.\n\n# Result\nThe result of our program is shown here (for citation dataset and facebook dataset, respectively).\n\n1 Citation\n\n![ROC_curve_citation](https://github.com/limaosen0/Variational-Graph-Auto-Encoders/blob/master/result/ROC_curve_citation_.png)\n\n2 Facebook\n\n![ROC_curve_facebook](https://github.com/limaosen0/Variational-Graph-Auto-Encoders/blob/master/result/ROC_curve_facebook_.png)\n\n# Tools and Modules\nTensorflow 1.5\n\nigraph-python\n\nnumpy, scipy.sparse and matplotlib\n\n# Notes\n\nYou can clone our program for practice, but if you use it in your published paper, please cite the paper \"Variational Graph Auto-Encoders\".\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flimaosen0%2FVariational-Graph-Auto-Encoders","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flimaosen0%2FVariational-Graph-Auto-Encoders","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flimaosen0%2FVariational-Graph-Auto-Encoders/lists"}