research-article

Community finding within the community set space

Authors:

Christian TrefftzAuthors Info & Claims

SNAKDD '13: Proceedings of the 7th Workshop on Social Network Mining and Analysis

Article No.: 13, Pages 1 - 9

https://doi.org/10.1145/2501025.2501032

Published: 11 August 2013 Publication History

Abstract

Community finding algorithms strive to find communities that have a higher connectivity within the communities than between them. Recently a framework called the community set space was introduced which provided a way to measure the quality of community sets. We present a new community finding algorithm, CHI, designed to minimize the violations defined by this framework. It will be shown that the CHI algorithm has similarities to kmeans. It is flexible and fast and can also be tuned to find certain types of communities. It is optimized for the community set framework and results so that it performs better than other algorithms within that framework.

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Cited By

Scripps JTrefftz CWolffe GFerguson RCao X(2019)Repel Communities and Multipartite NetworksComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_9(105-115)Online publication date: 26-Nov-2019
https://doi.org/10.1007/978-3-030-36687-2_9
Scripps JTrefftz CKurmas Z(2018)The difference between optimal and germane communitiesSocial Network Analysis and Mining10.1007/s13278-018-0522-18:1Online publication date: 21-Jun-2018
https://doi.org/10.1007/s13278-018-0522-1
Farasat MScripps JSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)LCHIProceedings of the International Conference on Web Intelligence10.1145/3106426.3106438(203-210)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106438

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cover image ACM Conferences

SNAKDD '13: Proceedings of the 7th Workshop on Social Network Mining and Analysis

August 2013

114 pages

ISBN:9781450323307

DOI:10.1145/2501025

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 August 2013

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KDD' 13

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11, 2013

Illinois, Chicago

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Cited By

Scripps JTrefftz CWolffe GFerguson RCao X(2019)Repel Communities and Multipartite NetworksComplex Networks and Their Applications VIII10.1007/978-3-030-36687-2_9(105-115)Online publication date: 26-Nov-2019
https://doi.org/10.1007/978-3-030-36687-2_9
Scripps JTrefftz CKurmas Z(2018)The difference between optimal and germane communitiesSocial Network Analysis and Mining10.1007/s13278-018-0522-18:1Online publication date: 21-Jun-2018
https://doi.org/10.1007/s13278-018-0522-1
Farasat MScripps JSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)LCHIProceedings of the International Conference on Web Intelligence10.1145/3106426.3106438(203-210)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106438

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