{"id":20410042,"url":"https://github.com/thumnlab/m-nmf","last_synced_at":"2026-03-08T01:33:41.355Z","repository":{"id":109778674,"uuid":"139700963","full_name":"THUMNLab/M-NMF","owner":"THUMNLab","description":"This is a sample implementation of \"Community Preserving Network Embedding\" (AAAI 2017).","archived":false,"fork":false,"pushed_at":"2018-07-04T09:43:22.000Z","size":1,"stargazers_count":4,"open_issues_count":0,"forks_count":1,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-01-15T13:09:22.445Z","etag":null,"topics":["community-detection","network-embedding","network-representation-learning","non-negative-matrix-factorization"],"latest_commit_sha":null,"homepage":"","language":"Matlab","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/THUMNLab.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,"publiccode":null,"codemeta":null}},"created_at":"2018-07-04T09:35:55.000Z","updated_at":"2020-09-22T01:17:37.000Z","dependencies_parsed_at":"2023-06-11T19:30:40.841Z","dependency_job_id":null,"html_url":"https://github.com/THUMNLab/M-NMF","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/THUMNLab%2FM-NMF","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2FM-NMF/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2FM-NMF/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/THUMNLab%2FM-NMF/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/THUMNLab","download_url":"https://codeload.github.com/THUMNLab/M-NMF/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241955039,"owners_count":20048405,"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":["community-detection","network-embedding","network-representation-learning","non-negative-matrix-factorization"],"created_at":"2024-11-15T05:44:42.214Z","updated_at":"2026-03-08T01:33:36.298Z","avatar_url":"https://github.com/THUMNLab.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TIMERS\nThis is a sample implementation of \"[Community Preserving Network Embedding](http://cuip.thumedialab.com/papers/NE-Community.pdf)\"(AAAI 2017).\n\n### Requirements\n```\nMATLAB \n```\n\n### Usage\nrun `MNMF.m` with matlab\n```\nfunction [U, M, H, C, L] = MNMF(S, M, U, H, C, B1, B2, alpha, beta, lambda)\n% The demo is written by Xiao Wang (wangxiao007@mail.tsinghua.edu.cn), and the details of the algorithm can be\n% found in \"Community Preserving Network Embedding\" (AAAI 2017).\n%%--------------input-----------------\n% S: S1+5*S2, the first- and second-order proximities (n-by-n);\n% M: the initialized basis matrix (n-by-m);\n% U: the initialized representations of nodes (n-by-m);\n% H: the initialized community indicator matrix (n-by-k);\n% C: the initialized representations of communities (k-by-m);\n% B1: the adjacency matrix B1(i,i)=0 (n-by-n);\n% B2: (k_i*k_j)/2e (n-by-n);\n% alpha, beta, lambda: the values of parameters, usually alpha and beta need to be tuned and we can set lambada = 1e9;\n%%-------------output------------------\n% U: the optimal representations of nodes;\n% M: the optimal basis matrix;\n% H: the optimal community indicator matrix;\n% C: the optimal representations of communities;\n% L: the final values of objective function.\n%%-------------------------------------\n```\n\n### Cite\nIf you find this code useful, please cite our paper:\n```\n@inproceedings{wang2017community,\n  title={Community Preserving Network Embedding},\n  author={Wang, Xiao and Cui, Peng and Wang, Jing and Pei, Jian and Zhu, Wenwu and Yang, Shiqiang},\n  booktitle={Proceedings of the 31st AAAI Conference on Artificial Intelligence},\n  year={2017}\n}\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthumnlab%2Fm-nmf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthumnlab%2Fm-nmf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthumnlab%2Fm-nmf/lists"}