{"id":13830107,"url":"https://github.com/tkipf/gae","last_synced_at":"2025-05-16T02:07:53.174Z","repository":{"id":37502979,"uuid":"94977683","full_name":"tkipf/gae","owner":"tkipf","description":"Implementation of Graph Auto-Encoders in TensorFlow","archived":false,"fork":false,"pushed_at":"2020-01-03T12:56:37.000Z","size":5216,"stargazers_count":1680,"open_issues_count":47,"forks_count":350,"subscribers_count":34,"default_branch":"master","last_synced_at":"2025-04-08T12:11:58.537Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/tkipf.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}},"created_at":"2017-06-21T07:48:31.000Z","updated_at":"2025-03-31T14:06:37.000Z","dependencies_parsed_at":"2022-07-21T09:03:02.163Z","dependency_job_id":null,"html_url":"https://github.com/tkipf/gae","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/tkipf%2Fgae","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tkipf%2Fgae/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tkipf%2Fgae/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tkipf%2Fgae/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tkipf","download_url":"https://codeload.github.com/tkipf/gae/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254453653,"owners_count":22073617,"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":"2024-08-04T10:00:55.638Z","updated_at":"2025-05-16T02:07:53.143Z","avatar_url":"https://github.com/tkipf.png","language":"Python","funding_links":[],"categories":["[Label Inference Methods](#content)","Python","其他_图神经网络GNN"],"sub_categories":["Graph Embedding Approaches","网络服务_其他"],"readme":"Graph Auto-Encoders\n============\n\nThis is a TensorFlow implementation of the (Variational) Graph Auto-Encoder model as described in our paper:\n \nT. N. Kipf, M. Welling, [Variational Graph Auto-Encoders](https://arxiv.org/abs/1611.07308), NIPS Workshop on Bayesian Deep Learning (2016)\n\nGraph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. \n\n![(Variational) Graph Auto-Encoder](figure.png)\n\nGAEs have successfully been used for:\n* Link prediction in large-scale relational data: M. Schlichtkrull \u0026 T. N. Kipf et al., [Modeling Relational Data with Graph Convolutional Networks](https://arxiv.org/abs/1703.06103) (2017),\n* Matrix completion / recommendation with side information: R. Berg et al., [Graph Convolutional Matrix Completion](https://arxiv.org/abs/1706.02263) (2017).\n\n\nGAEs are based on Graph Convolutional Networks (GCNs), a recent class of models for end-to-end (semi-)supervised learning on graphs:\n\nT. N. Kipf, M. Welling, [Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907), ICLR (2017). \n\nA high-level introduction is given in our blog post:\n\nThomas Kipf, [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (2016)\n\n\n\n## Installation\n\n```bash\npython setup.py install\n```\n\n## Requirements\n* TensorFlow (1.0 or later)\n* python 2.7\n* networkx\n* scikit-learn\n* scipy\n\n## Run the demo\n\n```bash\npython train.py\n```\n\n## Data\n\nIn order to use your own data, you have to provide \n* an N by N adjacency matrix (N is the number of nodes), and\n* an N by D feature matrix (D is the number of features per node) -- optional\n\nHave a look at the `load_data()` function in `input_data.py` for an example.\n\nIn this example, we load citation network data (Cora, Citeseer or Pubmed). The original datasets can be found here: http://linqs.cs.umd.edu/projects/projects/lbc/ and here (in a different format): https://github.com/kimiyoung/planetoid\n\nYou can specify a dataset as follows:\n\n```bash\npython train.py --dataset citeseer\n```\n\n(or by editing `train.py`)\n\n## Models\n\nYou can choose between the following models: \n* `gcn_ae`: Graph Auto-Encoder (with GCN encoder)\n* `gcn_vae`: Variational Graph Auto-Encoder (with GCN encoder)\n\n## Cite\n\nPlease cite our paper if you use this code in your own work:\n\n```\n@article{kipf2016variational,\n  title={Variational Graph Auto-Encoders},\n  author={Kipf, Thomas N and Welling, Max},\n  journal={NIPS Workshop on Bayesian Deep Learning},\n  year={2016}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftkipf%2Fgae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftkipf%2Fgae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftkipf%2Fgae/lists"}