{"id":19448494,"url":"https://github.com/neurodata/multiscale-network-test","last_synced_at":"2025-07-18T12:34:09.414Z","repository":{"id":140562792,"uuid":"65249271","full_name":"neurodata/Multiscale-Network-Test","owner":"neurodata","description":"Testing independence between network topology and nodal attributes","archived":false,"fork":false,"pushed_at":"2017-12-01T22:09:07.000Z","size":87296,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":8,"default_branch":"master","last_synced_at":"2025-02-25T08:53:43.631Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/neurodata.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-08-09T00:25:22.000Z","updated_at":"2018-03-07T17:51:42.000Z","dependencies_parsed_at":null,"dependency_job_id":"061ef972-35df-4493-b05e-cec6061813c9","html_url":"https://github.com/neurodata/Multiscale-Network-Test","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/neurodata/Multiscale-Network-Test","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FMultiscale-Network-Test","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FMultiscale-Network-Test/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FMultiscale-Network-Test/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FMultiscale-Network-Test/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/neurodata","download_url":"https://codeload.github.com/neurodata/Multiscale-Network-Test/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/neurodata%2FMultiscale-Network-Test/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260281568,"owners_count":22985629,"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":"2024-11-10T16:27:14.087Z","updated_at":"2025-06-17T03:07:10.634Z","avatar_url":"https://github.com/neurodata.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Multiscale Network Test (MNT)\n\nTesting independence between network toplogy and nodal attributes via diffusion maps applied to distance-based correlations.\n\n## MNT Use\n\nAs an input of MNT, we require `igraph` object G, a vector or matrix representing nodal attributes X, types of test statistics, use of diffusion maps, a range of Markov time used for diffusion maps, and the number of permutation samples.\n\nThe following example tests independence between network topology of karate club and each member's faction. \n\n```\nlibrary(igraphdata)\ndata(karate)\nG = karate\nX = V(karate)$Faction\nres \u003c- NetworkTest.diffusion.stat(G, X, option = 1, diffusion = TRUE,\n                                  t.range = c(0:5), n.perm = 500)\n```\nThe above prints out a list containing all the mgc statistics under the observations and under 500 permuations at Markov time t=0,1,2,..,5. The next command chooses the optimal diffusion map embeddings with dafault of t=3 and then we print out the p-value of network dependence test and optimal t. \n```\nres.optimal \u003c- print.stat.optimal(res, default.t = 4)\nprint(res.optimal$pvalue)\nprint(res.optimal$alt.t)\n```\n\n\n## Reference\n\nDetails of methods used for network dependence testing in the `mgc` package are described in [arXiv](https://arxiv.org/abs/1703.10136).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fmultiscale-network-test","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneurodata%2Fmultiscale-network-test","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneurodata%2Fmultiscale-network-test/lists"}