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dc.contributor.authorEstivill-Castro, V
dc.contributor.authorParsa, M
dc.description.abstractWe say that there is a community structure in a graph when the nodes of the graph can be partitioned into groups (communities) such that each group is internally more densely connected than with the rest of the graph. However, the challenge seems to specify what is to be dense, and what is relatively more connected (there seems to exist a similar situation to what is a cluster in unsupervised learning). Recently, Olsen [13] provided a general definition that is significantly more generic that others. We make two observations regarding such definition. First, we show that finding a community structure with k connected equal size communities is NP-complete. Then, we show that this problem can be solved efficiently on trees. Finally, we observed that every tree has a 2-community structure. This result is based on a reduction from an extremely popular heuristic (the Girvan-Neumann algorithm [12]) for detecting communities in large networks.
dc.publisherAssociation for Computing Machinery (ACM)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameACE '16
dc.relation.ispartofconferencetitleACM International Conference Proceeding Series
dc.relation.ispartoflocationCanberra, Australia
dc.subject.fieldofresearchAnalysis of Algorithms and Complexity
dc.titleHardness and tractability of detecting connected communities
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorEstivill-Castro, Vladimir

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    Contains papers delivered by Griffith authors at national and international conferences.

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