{"id":13689077,"url":"https://github.com/caesar0301/graphsim","last_synced_at":"2025-04-09T19:17:39.879Z","repository":{"id":57435885,"uuid":"45545870","full_name":"caesar0301/graphsim","owner":"caesar0301","description":"Graph similarity algorithms based on NetworkX.","archived":false,"fork":false,"pushed_at":"2019-08-27T11:21:57.000Z","size":58,"stargazers_count":169,"open_issues_count":4,"forks_count":25,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-04-09T19:17:35.257Z","etag":null,"topics":["graph","graph-similarity-algorithms","networkx","numpy","python","scientific","tacsim"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/caesar0301.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":".github/CODEOWNERS","security":null,"support":null}},"created_at":"2015-11-04T14:55:02.000Z","updated_at":"2025-03-04T11:57:39.000Z","dependencies_parsed_at":"2022-09-01T13:50:34.281Z","dependency_job_id":null,"html_url":"https://github.com/caesar0301/graphsim","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/caesar0301%2Fgraphsim","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesar0301%2Fgraphsim/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesar0301%2Fgraphsim/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/caesar0301%2Fgraphsim/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/caesar0301","download_url":"https://codeload.github.com/caesar0301/graphsim/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248094988,"owners_count":21046770,"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","graph-similarity-algorithms","networkx","numpy","python","scientific","tacsim"],"created_at":"2024-08-02T15:01:33.013Z","updated_at":"2025-04-09T19:17:39.850Z","avatar_url":"https://github.com/caesar0301.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"graphsim\n--------\n\nGraph similarity algorithms based on NetworkX.\n\n**BSD Licensed** \n\n[![Build Status](https://travis-ci.org/caesar0301/graphsim.svg?branch=master)](https://travis-ci.org/caesar0301/graphsim)\n[![PyPI](https://img.shields.io/pypi/l/graphsim.svg)](https://pypi.python.org/pypi/graphsim)\n[![PyPI](https://img.shields.io/pypi/pyversions/graphsim.svg)](https://pypi.python.org/pypi/graphsim)\n[![PyPI](https://img.shields.io/pypi/status/graphsim.svg)](https://pypi.python.org/pypi/graphsim)\n\nInstall\n-------\n\nFirst, install building tool:\n\n    $ yum install -y scons\n\nOn Mac OS:\n\n    $ brew install scons\n\nThen install graphsim via PyPI:\n\n    $ pip install -U graphsim\n    \n\nPermission Issues\n------------------\n\nBy default, `sudo` is required to give permission to install cpp modules into system `/usr/local/{lib,include}`. \n\nIf you prefer local installation, following instructions may help you:\n\n```bash\nexport LIBTACSIM_LIB_DIR=~/usr/lib/\nexport LIBTACSIM_INC_DIR=~/usr/include/\n\npip install -U graphsim\n```\n\nMake sure that the local directories are aware for C linkers:\n\n```bash\nexport LD_LIBRARY_PATH=~/usr/lib:$LD_LIBRARY_PATH\nexport C_INCLUDE_PATH=~/usr/include:$C_INCLUDE_PATH\nexport CPLUS_INCLUDE_PATH=~/usr/include:$CPLUS_INCLUDE_PATH\n```\n\n\nCoverage\n---------\n\n**NOTE**: `libtacsim` was tested on Ubuntu 12.04, Ubuntu 16.04, CentOS 6.5 and Mac OS 10.11.2, 10.13.2.\n\n\nUsage\n-----\n\n    \u003e\u003e\u003e import graphsim as gs\n\n\nSupported algorithms\n--------------------\n\n* `gs.ascos`: Asymmetric network Structure COntext Similarity, by Hung-Hsuan Chen et al. [[paper](https://ieeexplore.ieee.org/document/6785743)]\n* `gs.nsim_bvd04`: node-node similarity matrix, by Blondel et al. [[paper](https://dl.acm.org/citation.cfm?id=1035557)]\n* `gs.hits`: the hub and authority scores for nodes, by Kleinberg. [[paper](https://dl.acm.org/citation.cfm?id=324140)]\n* `gs.nsim_hs03`: node-node similarity with mismatch penalty, by Heymans et al. [[paper](https://www.ncbi.nlm.nih.gov/pubmed/12855450)]\n* `gs.simrank`: A Measure of Structural-Context Similarity, by Jeh et al. [[paper](https://dl.acm.org/citation.cfm?id=775126)]\n* `gs.simrank_bipartite`: SimRank for bipartite graphs, by Jeh et al. [[paper](https://dl.acm.org/citation.cfm?id=775126)]\n* `gs.tacsim`: Topology-Attributes Coupling Similarity, by Chen et al. [[paper](http://dx.doi.org/10.1016/j.pmcj.2017.02.001)]\n* `gs.tacsim_combined`: A combined topology-attributes coupling similarity, by Chen et al. [[paper](http://dx.doi.org/10.1016/j.pmcj.2017.02.001)]\n* `gs.tacsim_in_C`: an efficient implementation of TACSim in pure C.\n* `gs.tacsim_combined_in_C`: an efficient implementation of combined TACSim in pure C.\n\n\nSupported utilities\n-------------------\n\n* `gs.normalized`: L2 normalization of vectors, matrices or arrays.\n* `gs.node_edge_adjacency`: Obtain node-edge adjacency matrices in source and dest directions.\n\nCitation\n----------\n\n```tex\n@article{Chen2017,\n  title = \"Discovering and modeling meta-structures in human behavior from city-scale cellular data\",\n  journal = \"Pervasive and Mobile Computing \",\n  year = \"2017\",\n  issn = \"1574-1192\",\n  doi = \"http://dx.doi.org/10.1016/j.pmcj.2017.02.001\",\n  author = \"Xiaming Chen and Haiyang Wang and Siwei Qiang and Yongkun Wang and Yaohui Jin\"\n}\n```\n\nAuthor\n------\n\nXiaming Chen \u003cchenxm35@gmail.com\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaesar0301%2Fgraphsim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcaesar0301%2Fgraphsim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcaesar0301%2Fgraphsim/lists"}