{"id":20517240,"url":"https://github.com/trustagi-lab/merit","last_synced_at":"2025-04-14T00:54:39.302Z","repository":{"id":49747296,"uuid":"331966310","full_name":"TrustAGI-Lab/MERIT","owner":"TrustAGI-Lab","description":"[IJCAI 2021] A PyTorch implementation of \"Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning\".","archived":false,"fork":false,"pushed_at":"2022-10-13T11:10:18.000Z","size":32766,"stargazers_count":40,"open_issues_count":2,"forks_count":8,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-27T14:55:28.858Z","etag":null,"topics":["graph-neural-networks","graph-representation-learning","graph-self-supervised-learning","self-supervised-distillation"],"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":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-01-22T14:15:56.000Z","updated_at":"2024-12-25T06:42:00.000Z","dependencies_parsed_at":"2023-01-19T23:47:29.322Z","dependency_job_id":null,"html_url":"https://github.com/TrustAGI-Lab/MERIT","commit_stats":null,"previous_names":["trustagi-lab/merit"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FMERIT","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FMERIT/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FMERIT/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TrustAGI-Lab%2FMERIT/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TrustAGI-Lab","download_url":"https://codeload.github.com/TrustAGI-Lab/MERIT/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248804783,"owners_count":21164131,"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":["graph-neural-networks","graph-representation-learning","graph-self-supervised-learning","self-supervised-distillation"],"created_at":"2024-11-15T21:34:27.569Z","updated_at":"2025-04-14T00:54:39.277Z","avatar_url":"https://github.com/TrustAGI-Lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MERIT\n\n\u003cp align=\"center\"\u003e\n\u003cimg src=\"./merit.png\" width=\"600\"\u003e\n\u003c/p\u003e\n\nA PyTorch implementation of our IJCAI-21 paper [Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning](https://arxiv.org/abs/2105.05682).\n\n## Dependencies\n+ Python (\u003e=3.6)\n+ PyTorch (\u003e=1.7.1)\n+ NumPy (\u003e=1.19.2)\n+ Scikit-Learn (\u003e=0.24.1)\n+ Scipy (\u003e=1.6.1)\n+ Networkx (\u003e=2.5)\n\nTo install all dependencies:\n```\npip install -r requirements.txt\n```\n\n## Usage\nHere we provide the implementation of MERIT along with Cora and Citeseer dataset.\n\n+ To train and evaluate on Cora:\n```\npython run_cora.py\n```\n\n+ To train and evaluate on Citeseer:\n```\npython run_citeseer.py\n```\n\n## Citation\nIf you use our code in your research, please cite the following article:\n```\n@inproceedings{Jin2021MultiScaleCS,\n  title={Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning},\n  author={Ming Jin and Yizhen Zheng and Yuan-Fang Li and Chen Gong and Chuan Zhou and Shirui Pan},\n  booktitle={The 30th International Joint Conference on Artificial Intelligence (IJCAI)},\n  year={2021}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrustagi-lab%2Fmerit","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftrustagi-lab%2Fmerit","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftrustagi-lab%2Fmerit/lists"}