rMultiNet: An R Package For Multilayer Networks Analysis
This paper develops an R package rMultiNet to analyze multilayer network data. We provide two general frameworks from recent literature, e.g. mixture multilayer stochastic block model(MMSBM) and mixture multilayer latent space model(MMLSM) to generate the multilayer network. We also provide several...
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creator | Li, Ting Lyu, Zhongyuan Ren, Chenyu Xia, Dong |
description | This paper develops an R package rMultiNet to analyze multilayer network
data. We provide two general frameworks from recent literature, e.g. mixture
multilayer stochastic block model(MMSBM) and mixture multilayer latent space
model(MMLSM) to generate the multilayer network. We also provide several
methods to reveal the embedding of both nodes and layers followed by further
data analysis methods, such as clustering. Three real data examples are
processed in the package. The source code of rMultiNet is available at
https://github.com/ChenyuzZZ73/rMultiNet. |
doi_str_mv | 10.48550/arxiv.2302.04437 |
format | Article |
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data. We provide two general frameworks from recent literature, e.g. mixture
multilayer stochastic block model(MMSBM) and mixture multilayer latent space
model(MMLSM) to generate the multilayer network. We also provide several
methods to reveal the embedding of both nodes and layers followed by further
data analysis methods, such as clustering. Three real data examples are
processed in the package. The source code of rMultiNet is available at
https://github.com/ChenyuzZZ73/rMultiNet.</description><identifier>DOI: 10.48550/arxiv.2302.04437</identifier><language>eng</language><subject>Computer Science - Learning ; Statistics - Applications ; Statistics - Machine Learning</subject><creationdate>2023-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,782,887</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2302.04437$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2302.04437$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Ting</creatorcontrib><creatorcontrib>Lyu, Zhongyuan</creatorcontrib><creatorcontrib>Ren, Chenyu</creatorcontrib><creatorcontrib>Xia, Dong</creatorcontrib><title>rMultiNet: An R Package For Multilayer Networks Analysis</title><description>This paper develops an R package rMultiNet to analyze multilayer network
data. We provide two general frameworks from recent literature, e.g. mixture
multilayer stochastic block model(MMSBM) and mixture multilayer latent space
model(MMLSM) to generate the multilayer network. We also provide several
methods to reveal the embedding of both nodes and layers followed by further
data analysis methods, such as clustering. Three real data examples are
processed in the package. The source code of rMultiNet is available at
https://github.com/ChenyuzZZ73/rMultiNet.</description><subject>Computer Science - Learning</subject><subject>Statistics - Applications</subject><subject>Statistics - Machine Learning</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tuwjAURL3pAlE-gBX-gQRzfWM73SHES4IWoeyjm8RBESkgh1f-HjdlNYs5M5phbDgRIZooEmNyz-oeghQQCkSpe8y47a2-Vt_2-sWnJ77nO8qPdLB8cXa8s2pqreMeeJzdsfEQ1W1TNZ_so6S6sYO39lmymCezVbD5Wa5n001ASuvAaAAsNVAMBVCuVIbWKMxQxVTKQscqE7k1AgRSkZfgQ54qJEXo45mUfTb6r-2mpxdX_ZJr078LaXdBvgAJCkAV</recordid><startdate>20230208</startdate><enddate>20230208</enddate><creator>Li, Ting</creator><creator>Lyu, Zhongyuan</creator><creator>Ren, Chenyu</creator><creator>Xia, Dong</creator><scope>AKY</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20230208</creationdate><title>rMultiNet: An R Package For Multilayer Networks Analysis</title><author>Li, Ting ; Lyu, Zhongyuan ; Ren, Chenyu ; Xia, Dong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a677-87224f72a92d2ac66b4e864b469af3d796b0ce80204adcf2a67ac6d3a54677b33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Learning</topic><topic>Statistics - Applications</topic><topic>Statistics - Machine Learning</topic><toplevel>online_resources</toplevel><creatorcontrib>Li, Ting</creatorcontrib><creatorcontrib>Lyu, Zhongyuan</creatorcontrib><creatorcontrib>Ren, Chenyu</creatorcontrib><creatorcontrib>Xia, Dong</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Ting</au><au>Lyu, Zhongyuan</au><au>Ren, Chenyu</au><au>Xia, Dong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>rMultiNet: An R Package For Multilayer Networks Analysis</atitle><date>2023-02-08</date><risdate>2023</risdate><abstract>This paper develops an R package rMultiNet to analyze multilayer network
data. We provide two general frameworks from recent literature, e.g. mixture
multilayer stochastic block model(MMSBM) and mixture multilayer latent space
model(MMLSM) to generate the multilayer network. We also provide several
methods to reveal the embedding of both nodes and layers followed by further
data analysis methods, such as clustering. Three real data examples are
processed in the package. The source code of rMultiNet is available at
https://github.com/ChenyuzZZ73/rMultiNet.</abstract><doi>10.48550/arxiv.2302.04437</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Learning Statistics - Applications Statistics - Machine Learning |
title | rMultiNet: An R Package For Multilayer Networks Analysis |
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