Stochastic block models for multiplex networks: an application to a multilevel network of researchers
Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with res...
Gespeichert in:
Veröffentlicht in: | Journal of the Royal Statistical Society. Series A, Statistics in society Statistics in society, 2017-01, Vol.180 (1), p.295-314 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 314 |
---|---|
container_issue | 1 |
container_start_page | 295 |
container_title | Journal of the Royal Statistical Society. Series A, Statistics in society |
container_volume | 180 |
creator | Barbillon, Pierre Donnet, Sophie Lazega, Emmanuel Bar-Hen, Avner |
description | Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation-maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together. |
doi_str_mv | 10.1111/rssa.12193 |
format | Article |
fullrecord | <record><control><sourceid>jstor_hal_p</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_01520820v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><jstor_id>44682563</jstor_id><sourcerecordid>44682563</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5533-a744169388c1dd87ca25b011703b2cd1dd7afe145395b6fdd0830f17e13ab99f3</originalsourceid><addsrcrecordid>eNp90U1LxDAQBuAgCq4fF-9CwIsK1UzTtIm3RfyCBcFV8BbSNGW7ZpuadFf992atevDgXALD84ZkBqEDIGcQ69yHoM4gBUE30AiyvEgEZ8-baEREniUgBN9GOyHMybqKYoTMtHd6pkLfaFxap1_wwlXGBlw7jxdL2zedNe-4Nf2b8y_hAqsWq66zjVZ941rcO6wGZ83K2B-IXY29CUZ5PTM-7KGtWtlg9r_PXfR0ffV4eZtM7m_uLseTRDNGaaKKLINcUM41VBUvtEpZSQAKQstUV7FXqNpAxqhgZV5XFeGU1FAYoKoUoqa76GS4d6as7HyzUP5DOtXI2_FErnsEWEp4SlYQ7fFgO-9elyb0ctEEbaxVrXHLIIFzQighKYv06A-du6Vv408kiDROmIPI_1WRMMHzL3U6KO1dCN7Uv-8EItc7lOsdyq8dRgwDfovj_fhHyofpdPyTORwy89A7_5vJspynLKf0E-Onp38</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1858598696</pqid></control><display><type>article</type><title>Stochastic block models for multiplex networks: an application to a multilevel network of researchers</title><source>EBSCOhost Business Source Complete</source><source>Access via Wiley Online Library</source><source>JSTOR Archive Collection A-Z Listing</source><source>Oxford University Press Journals All Titles (1996-Current)</source><creator>Barbillon, Pierre ; Donnet, Sophie ; Lazega, Emmanuel ; Bar-Hen, Avner</creator><creatorcontrib>Barbillon, Pierre ; Donnet, Sophie ; Lazega, Emmanuel ; Bar-Hen, Avner</creatorcontrib><description>Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation-maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.</description><identifier>ISSN: 0964-1998</identifier><identifier>EISSN: 1467-985X</identifier><identifier>DOI: 10.1111/rssa.12193</identifier><language>eng</language><publisher>Oxford: John Wiley & Sons Ltd</publisher><subject>Bivariate stochastic block model ; Cancer ; Clustering ; Consistency ; Criteria ; Estimates ; Humanities and Social Sciences ; Joints ; Matrix ; Multilevel ; Multilevel or multiplex networks ; Multiplexing ; Networks ; Parameter estimation ; Probability ; Researchers ; Social network ; Social sciences ; Sociology ; Stochastic models ; Stochasticity</subject><ispartof>Journal of the Royal Statistical Society. Series A, Statistics in society, 2017-01, Vol.180 (1), p.295-314</ispartof><rights>Copyright © 2017 The Royal Statistical Society and John Wiley & Sons Ltd.</rights><rights>2016 Royal Statistical Society</rights><rights>Copyright Blackwell Publishing Ltd. Jan 2017</rights><rights>Copyright © 2017 The Royal Statistical Society and Blackwell Publishing Ltd</rights><rights>Attribution - ShareAlike</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5533-a744169388c1dd87ca25b011703b2cd1dd7afe145395b6fdd0830f17e13ab99f3</citedby><cites>FETCH-LOGICAL-c5533-a744169388c1dd87ca25b011703b2cd1dd7afe145395b6fdd0830f17e13ab99f3</cites><orcidid>0000-0002-7766-7693 ; 0000-0002-4449-8117 ; 0000-0001-8844-6426 ; 0000-0003-4370-7316</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/44682563$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/44682563$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,780,784,803,885,1417,27924,27925,45574,45575,58017,58250</link.rule.ids><backlink>$$Uhttps://sciencespo.hal.science/hal-01520820$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Barbillon, Pierre</creatorcontrib><creatorcontrib>Donnet, Sophie</creatorcontrib><creatorcontrib>Lazega, Emmanuel</creatorcontrib><creatorcontrib>Bar-Hen, Avner</creatorcontrib><title>Stochastic block models for multiplex networks: an application to a multilevel network of researchers</title><title>Journal of the Royal Statistical Society. Series A, Statistics in society</title><description>Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation-maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.</description><subject>Bivariate stochastic block model</subject><subject>Cancer</subject><subject>Clustering</subject><subject>Consistency</subject><subject>Criteria</subject><subject>Estimates</subject><subject>Humanities and Social Sciences</subject><subject>Joints</subject><subject>Matrix</subject><subject>Multilevel</subject><subject>Multilevel or multiplex networks</subject><subject>Multiplexing</subject><subject>Networks</subject><subject>Parameter estimation</subject><subject>Probability</subject><subject>Researchers</subject><subject>Social network</subject><subject>Social sciences</subject><subject>Sociology</subject><subject>Stochastic models</subject><subject>Stochasticity</subject><issn>0964-1998</issn><issn>1467-985X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp90U1LxDAQBuAgCq4fF-9CwIsK1UzTtIm3RfyCBcFV8BbSNGW7ZpuadFf992atevDgXALD84ZkBqEDIGcQ69yHoM4gBUE30AiyvEgEZ8-baEREniUgBN9GOyHMybqKYoTMtHd6pkLfaFxap1_wwlXGBlw7jxdL2zedNe-4Nf2b8y_hAqsWq66zjVZ941rcO6wGZ83K2B-IXY29CUZ5PTM-7KGtWtlg9r_PXfR0ffV4eZtM7m_uLseTRDNGaaKKLINcUM41VBUvtEpZSQAKQstUV7FXqNpAxqhgZV5XFeGU1FAYoKoUoqa76GS4d6as7HyzUP5DOtXI2_FErnsEWEp4SlYQ7fFgO-9elyb0ctEEbaxVrXHLIIFzQighKYv06A-du6Vv408kiDROmIPI_1WRMMHzL3U6KO1dCN7Uv-8EItc7lOsdyq8dRgwDfovj_fhHyofpdPyTORwy89A7_5vJspynLKf0E-Onp38</recordid><startdate>201701</startdate><enddate>201701</enddate><creator>Barbillon, Pierre</creator><creator>Donnet, Sophie</creator><creator>Lazega, Emmanuel</creator><creator>Bar-Hen, Avner</creator><general>John Wiley & Sons Ltd</general><general>Oxford University Press</general><general>Royal Statistical Society</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8BJ</scope><scope>8FD</scope><scope>FQK</scope><scope>JBE</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><scope>BXJBU</scope><scope>IHQJB</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-7766-7693</orcidid><orcidid>https://orcid.org/0000-0002-4449-8117</orcidid><orcidid>https://orcid.org/0000-0001-8844-6426</orcidid><orcidid>https://orcid.org/0000-0003-4370-7316</orcidid></search><sort><creationdate>201701</creationdate><title>Stochastic block models for multiplex networks: an application to a multilevel network of researchers</title><author>Barbillon, Pierre ; Donnet, Sophie ; Lazega, Emmanuel ; Bar-Hen, Avner</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5533-a744169388c1dd87ca25b011703b2cd1dd7afe145395b6fdd0830f17e13ab99f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Bivariate stochastic block model</topic><topic>Cancer</topic><topic>Clustering</topic><topic>Consistency</topic><topic>Criteria</topic><topic>Estimates</topic><topic>Humanities and Social Sciences</topic><topic>Joints</topic><topic>Matrix</topic><topic>Multilevel</topic><topic>Multilevel or multiplex networks</topic><topic>Multiplexing</topic><topic>Networks</topic><topic>Parameter estimation</topic><topic>Probability</topic><topic>Researchers</topic><topic>Social network</topic><topic>Social sciences</topic><topic>Sociology</topic><topic>Stochastic models</topic><topic>Stochasticity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Barbillon, Pierre</creatorcontrib><creatorcontrib>Donnet, Sophie</creatorcontrib><creatorcontrib>Lazega, Emmanuel</creatorcontrib><creatorcontrib>Bar-Hen, Avner</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>Technology Research Database</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société</collection><collection>HAL-SHS: Archive ouverte en Sciences de l'Homme et de la Société (Open Access)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of the Royal Statistical Society. Series A, Statistics in society</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Barbillon, Pierre</au><au>Donnet, Sophie</au><au>Lazega, Emmanuel</au><au>Bar-Hen, Avner</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stochastic block models for multiplex networks: an application to a multilevel network of researchers</atitle><jtitle>Journal of the Royal Statistical Society. Series A, Statistics in society</jtitle><date>2017-01</date><risdate>2017</risdate><volume>180</volume><issue>1</issue><spage>295</spage><epage>314</epage><pages>295-314</pages><issn>0964-1998</issn><eissn>1467-985X</eissn><abstract>Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation-maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.</abstract><cop>Oxford</cop><pub>John Wiley & Sons Ltd</pub><doi>10.1111/rssa.12193</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-7766-7693</orcidid><orcidid>https://orcid.org/0000-0002-4449-8117</orcidid><orcidid>https://orcid.org/0000-0001-8844-6426</orcidid><orcidid>https://orcid.org/0000-0003-4370-7316</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0964-1998 |
ispartof | Journal of the Royal Statistical Society. Series A, Statistics in society, 2017-01, Vol.180 (1), p.295-314 |
issn | 0964-1998 1467-985X |
language | eng |
recordid | cdi_hal_primary_oai_HAL_hal_01520820v1 |
source | EBSCOhost Business Source Complete; Access via Wiley Online Library; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current) |
subjects | Bivariate stochastic block model Cancer Clustering Consistency Criteria Estimates Humanities and Social Sciences Joints Matrix Multilevel Multilevel or multiplex networks Multiplexing Networks Parameter estimation Probability Researchers Social network Social sciences Sociology Stochastic models Stochasticity |
title | Stochastic block models for multiplex networks: an application to a multilevel network of researchers |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T07%3A00%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-jstor_hal_p&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Stochastic%20block%20models%20for%20multiplex%20networks:%20an%20application%20to%20a%20multilevel%20network%20of%20researchers&rft.jtitle=Journal%20of%20the%20Royal%20Statistical%20Society.%20Series%20A,%20Statistics%20in%20society&rft.au=Barbillon,%20Pierre&rft.date=2017-01&rft.volume=180&rft.issue=1&rft.spage=295&rft.epage=314&rft.pages=295-314&rft.issn=0964-1998&rft.eissn=1467-985X&rft_id=info:doi/10.1111/rssa.12193&rft_dat=%3Cjstor_hal_p%3E44682563%3C/jstor_hal_p%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1858598696&rft_id=info:pmid/&rft_jstor_id=44682563&rfr_iscdi=true |