{"id":13483595,"url":"https://github.com/danison2/MLC-code","last_synced_at":"2025-03-27T14:31:29.162Z","repository":{"id":40978126,"uuid":"213894774","full_name":"danison2/MLC-code","owner":"danison2","description":"This code is based on the paper: A Nonenegative Matrix Factorization Approach for Multiple Local Community Detection published in the ASONAM conference in 2018.","archived":false,"fork":false,"pushed_at":"2024-06-17T23:07:12.000Z","size":8951,"stargazers_count":3,"open_issues_count":2,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-10-30T17:48:01.988Z","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/danison2.png","metadata":{"files":{"readme":"readme.txt","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":"2019-10-09T11:01:15.000Z","updated_at":"2023-05-25T07:48:18.000Z","dependencies_parsed_at":"2024-10-30T17:41:16.760Z","dependency_job_id":null,"html_url":"https://github.com/danison2/MLC-code","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/danison2%2FMLC-code","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danison2%2FMLC-code/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danison2%2FMLC-code/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danison2%2FMLC-code/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/danison2","download_url":"https://codeload.github.com/danison2/MLC-code/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245863100,"owners_count":20684788,"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":"2024-07-31T17:01:13.167Z","updated_at":"2025-03-27T14:31:26.338Z","avatar_url":"https://github.com/danison2.png","language":"Python","readme":"This code is based on the paper: A Nonenegative Matrix Factorization Approach for Multiple Local Community Detection published in the ASONAM conference in 2018.\n\n\nTo run  the code with the sample Amazon network:\n(1) with cmd go to the code folder\n(2) pip install -r requirements.txt\n(3) go to MLC folder\n(4)python MLC.py\n(5) go to MLC-code folder, you will find the conductance results in the Cond folder and the F1 results in the F1 folder.\n\nNOTE: \n\n(a) graphA is Amazon while graphD is DBLP.\n\n(b) To run this code on a different graph, change the following variables:\n\tgraphFiles=['graphA.txt'] \t#Amazon\n\tcommunityFile='newComA.txt' \t#cleaned ground-truth communities with duplicates removed\n        seedsFiles=['seedsA3.txt'] \t#seeds that belong to three communities\n        delimiter = \"\\t\" \t\t#delimiter of the graph's edge list. For some graphs, it is just blank space \" \"\n\nThe data folder contains other sample graphs and seeds, and their ground-truth communities. \nKarate club is not included in the graphs folder as it can be generated using: G = nx.karate_club_graph()\n\n\n\nCitation (BibTex format):\n\n@article{Kamuhanda2018ANM,\n  title={A Nonnegative Matrix Factorization Approach for Multiple Local Community Detection},\n  author={Dany Kamuhanda and Kun He},\n  journal={2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},\n  year={2018},\n  pages={642-649}\n}","funding_links":[],"categories":["Factorization"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanison2%2FMLC-code","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanison2%2FMLC-code","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanison2%2FMLC-code/lists"}