On the Statistical Significance of a Community Structure
The community structure typically refers to the existence of a network partition in terms of a set of non-overlapping dense sub-graphs, where each sub-graph is called a community and there are few links between different communities. The detection of community structure is able to provide additional...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2023-03, Vol.35 (3), p.2887-2900 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | He, Zengyou Wei, Xiaoqi Chen, Wenfang Liu, Yan |
| description | The community structure typically refers to the existence of a network partition in terms of a set of non-overlapping dense sub-graphs, where each sub-graph is called a community and there are few links between different communities. The detection of community structure is able to provide additional knowledge on the organization mechanism of the network and its characteristics. Despite decades of developments in community detection algorithms, how to determine whether a given community structure is true or not in a statistically sound manner still remains unresolved. In this paper, we derive an analytical upper bound on the p p -value of a community structure under the configuration model. To demonstrate its effectiveness on community structure validation, we further develop a community detection algorithm in which the p p -value upper bound is used as the objective function. Experimental results on both real networks and simulated networks show that our algorithm outperforms prior state-of-the-art community detection methods. |
| doi_str_mv | 10.1109/TKDE.2021.3125330 |
| format | Article |
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The detection of community structure is able to provide additional knowledge on the organization mechanism of the network and its characteristics. Despite decades of developments in community detection algorithms, how to determine whether a given community structure is true or not in a statistically sound manner still remains unresolved. In this paper, we derive an analytical upper bound on the <inline-formula><tex-math notation="LaTeX">p</tex-math> <mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq1-3125330.gif"/> </inline-formula>-value of a community structure under the configuration model. To demonstrate its effectiveness on community structure validation, we further develop a community detection algorithm in which the <inline-formula><tex-math notation="LaTeX">p</tex-math> <mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq2-3125330.gif"/> </inline-formula>-value upper bound is used as the objective function. Experimental results on both real networks and simulated networks show that our algorithm outperforms prior state-of-the-art community detection methods.]]></description><identifier>ISSN: 1041-4347</identifier><identifier>EISSN: 1558-2191</identifier><identifier>DOI: 10.1109/TKDE.2021.3125330</identifier><identifier>CODEN: ITKEEH</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Analytical models ; Community detection ; Detection algorithms ; Indexes ; Linear programming ; Measurement ; network partition ; random graphs ; Sampling methods ; Statistical methods ; Statistical significance ; Upper bound ; Upper bounds</subject><ispartof>IEEE transactions on knowledge and data engineering, 2023-03, Vol.35 (3), p.2887-2900</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c288t-59f33e98523598c8e078c0519405a60dc722c0386c442f7c5965ce9b081467353</cites><orcidid>0000-0001-9526-8816 ; 0000-0002-1386-812X ; 0000-0003-2257-063X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9606604$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9606604$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>He, Zengyou</creatorcontrib><creatorcontrib>Wei, Xiaoqi</creatorcontrib><creatorcontrib>Chen, Wenfang</creatorcontrib><creatorcontrib>Liu, Yan</creatorcontrib><title>On the Statistical Significance of a Community Structure</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description><![CDATA[The community structure typically refers to the existence of a network partition in terms of a set of non-overlapping dense sub-graphs, where each sub-graph is called a community and there are few links between different communities. The detection of community structure is able to provide additional knowledge on the organization mechanism of the network and its characteristics. Despite decades of developments in community detection algorithms, how to determine whether a given community structure is true or not in a statistically sound manner still remains unresolved. In this paper, we derive an analytical upper bound on the <inline-formula><tex-math notation="LaTeX">p</tex-math> <mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq1-3125330.gif"/> </inline-formula>-value of a community structure under the configuration model. To demonstrate its effectiveness on community structure validation, we further develop a community detection algorithm in which the <inline-formula><tex-math notation="LaTeX">p</tex-math> <mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq2-3125330.gif"/> </inline-formula>-value upper bound is used as the objective function. Experimental results on both real networks and simulated networks show that our algorithm outperforms prior state-of-the-art community detection methods.]]></description><subject>Algorithms</subject><subject>Analytical models</subject><subject>Community detection</subject><subject>Detection algorithms</subject><subject>Indexes</subject><subject>Linear programming</subject><subject>Measurement</subject><subject>network partition</subject><subject>random graphs</subject><subject>Sampling methods</subject><subject>Statistical methods</subject><subject>Statistical significance</subject><subject>Upper bound</subject><subject>Upper bounds</subject><issn>1041-4347</issn><issn>1558-2191</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kMtOwzAQRS0EEqXwAYhNJNYpM357iUp5iEpdtKytYBxw1STFdhb9exK1YjV3ce4d6RByizBDBPOweX9azChQnDGkgjE4IxMUQpcUDZ4PGTiWnHF1Sa5S2gKAVhonRK_aIv_4Yp2rHFIOrtoV6_DdhnqIrfNFVxdVMe-apm9DPgxc7F3uo78mF3W1S_7mdKfk43mxmb-Wy9XL2_xxWTqqdS6FqRnzRgvKhNFOe1DagUDDQVQSvpyi1AHT0nFOa-WEkcJ58wkauVRMsCm5P-7uY_fb-5TttutjO7y0VCnGBeMgBwqPlItdStHXdh9DU8WDRbCjIDsKsqMgexI0dO6OneC9_-eNBCmBsz-QwV6l</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>He, Zengyou</creator><creator>Wei, Xiaoqi</creator><creator>Chen, Wenfang</creator><creator>Liu, Yan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-9526-8816</orcidid><orcidid>https://orcid.org/0000-0002-1386-812X</orcidid><orcidid>https://orcid.org/0000-0003-2257-063X</orcidid></search><sort><creationdate>20230301</creationdate><title>On the Statistical Significance of a Community Structure</title><author>He, Zengyou ; Wei, Xiaoqi ; Chen, Wenfang ; Liu, Yan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c288t-59f33e98523598c8e078c0519405a60dc722c0386c442f7c5965ce9b081467353</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Analytical models</topic><topic>Community detection</topic><topic>Detection algorithms</topic><topic>Indexes</topic><topic>Linear programming</topic><topic>Measurement</topic><topic>network partition</topic><topic>random graphs</topic><topic>Sampling methods</topic><topic>Statistical methods</topic><topic>Statistical significance</topic><topic>Upper bound</topic><topic>Upper bounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>He, Zengyou</creatorcontrib><creatorcontrib>Wei, Xiaoqi</creatorcontrib><creatorcontrib>Chen, Wenfang</creatorcontrib><creatorcontrib>Liu, Yan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</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><jtitle>IEEE transactions on knowledge and data engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>He, Zengyou</au><au>Wei, Xiaoqi</au><au>Chen, Wenfang</au><au>Liu, Yan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the Statistical Significance of a Community Structure</atitle><jtitle>IEEE transactions on knowledge and data engineering</jtitle><stitle>TKDE</stitle><date>2023-03-01</date><risdate>2023</risdate><volume>35</volume><issue>3</issue><spage>2887</spage><epage>2900</epage><pages>2887-2900</pages><issn>1041-4347</issn><eissn>1558-2191</eissn><coden>ITKEEH</coden><abstract><![CDATA[The community structure typically refers to the existence of a network partition in terms of a set of non-overlapping dense sub-graphs, where each sub-graph is called a community and there are few links between different communities. The detection of community structure is able to provide additional knowledge on the organization mechanism of the network and its characteristics. Despite decades of developments in community detection algorithms, how to determine whether a given community structure is true or not in a statistically sound manner still remains unresolved. In this paper, we derive an analytical upper bound on the <inline-formula><tex-math notation="LaTeX">p</tex-math> <mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq1-3125330.gif"/> </inline-formula>-value of a community structure under the configuration model. To demonstrate its effectiveness on community structure validation, we further develop a community detection algorithm in which the <inline-formula><tex-math notation="LaTeX">p</tex-math> <mml:math><mml:mi>p</mml:mi></mml:math><inline-graphic xlink:href="he-ieq2-3125330.gif"/> </inline-formula>-value upper bound is used as the objective function. Experimental results on both real networks and simulated networks show that our algorithm outperforms prior state-of-the-art community detection methods.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TKDE.2021.3125330</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0001-9526-8816</orcidid><orcidid>https://orcid.org/0000-0002-1386-812X</orcidid><orcidid>https://orcid.org/0000-0003-2257-063X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analytical models Community detection Detection algorithms Indexes Linear programming Measurement network partition random graphs Sampling methods Statistical methods Statistical significance Upper bound Upper bounds |
| title | On the Statistical Significance of a Community Structure |
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