{"id":10450086,"url":"https://github.com/TrustAGI-Lab/ARGA","last_synced_at":"2025-09-09T17:31:46.040Z","repository":{"id":111580092,"uuid":"142285615","full_name":"TrustAGI-Lab/ARGA","owner":"TrustAGI-Lab","description":"This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper:   Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., \u0026 Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].","archived":false,"fork":false,"pushed_at":"2022-02-28T08:59:28.000Z","size":5424,"stargazers_count":185,"open_issues_count":14,"forks_count":60,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-05-19T00:37:45.715Z","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":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TrustAGI-Lab.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}},"created_at":"2018-07-25T10:35:17.000Z","updated_at":"2024-05-10T08:54:56.000Z","dependencies_parsed_at":"2023-04-27T04:17:46.323Z","dependency_job_id":null,"html_url":"https://github.com/TrustAGI-Lab/ARGA","commit_stats":null,"previous_names":["trustagi-lab/arga"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FARGA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FARGA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FARGA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FARGA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TrustAGI-Lab","download_url":"https://codeload.github.com/TrustAGI-Lab/ARGA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":232435148,"owners_count":18522670,"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":"2024-05-29T06:05:03.512Z","updated_at":"2025-01-04T06:31:38.078Z","avatar_url":"https://github.com/TrustAGI-Lab.png","language":"Python","funding_links":[],"categories":["其他_图神经网络GNN","TensorFlow Implementations"],"sub_categories":["网络服务_其他"],"readme":"Adversarially Regularized Graph Autoencoder (ARGA)\n============\n\nThis is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper:\n \nPan, S., Hu, R., Long, G., Jiang, J., Yao, L., \u0026 Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf], published in IJCAI 2018: 2609-2615.\n\n![Construction of ARGA](https://github.com/Ruiqi-Hu/ARGA/blob/master/ARGA_FLOW.jpg)\n\nWe borrowed part of code from T. N. Kipf, M. Welling, Variational Graph Auto-Encoders [https://github.com/tkipf/gae]\n\n\n## Installation\n\n```bash\npip install -r requirements.txt\n```\n\n## Requirements\n* TensorFlow (1.0 or later)\n* python 2.7\n* networkx\n* scikit-learn\n* scipy\n\n## Run from\n\n```bash\npython run.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\n## Models\n\nYou can choose between the following models: \n* `arga_ae`: Adversarially Regularised Graph Auto-Encoder\n* `arga_vae`: Adversarially Regularised Variational Graph Auto-Encoder \n\n## Cite\n\nPlease cite following papers if you use this code in your own work:\n\n```\n@inproceedings{pan2018adversarially,\n  title={Adversarially Regularized Graph Autoencoder for Graph Embedding.},\n  author={Pan, Shirui and Hu, Ruiqi and Long, Guodong and Jiang, Jing and Yao, Lina and Zhang, Chengqi},\n  booktitle={IJCAI},\n  pages={2609--2615},\n  year={2018}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTrustAGI-Lab%2FARGA","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FTrustAGI-Lab%2FARGA","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTrustAGI-Lab%2FARGA/lists"}