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Conference Paper: Discovering Maximal Motif Cliques in Large Heterogeneous Information Networks

TitleDiscovering Maximal Motif Cliques in Large Heterogeneous Information Networks
Authors
KeywordsClique
Heterogeneous information networks
Motif
Issue Date2019
PublisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178
Citation
35th IEEE International Conference on Data Engineering (ICDE 2019), Macau, China, 8-11 April 2019, p. 746-757 How to Cite?
AbstractWe study the discovery of cliques (or 'complete' subgraphs) in heterogeneous information networks (HINs). Existing clique-finding solutions often ignore the rich semantics of HINs. We propose motif clique, or m-clique, which redefines subgraph completeness with respect to a given motif. A motif, essentially a small subgraph pattern, is a fundamental building block of an HIN. The m-clique concept is general and allows us to analyse 'complete' subgraphs in an HIN with respect to desired high-order connection patterns. We further investigate the maximal m-clique enumeration problem (MMCE), which finds all maximal m-cliques not contained in any other m-cliques. Because MMCE is NP-hard, developing an accurate and efficient solution for MMCE is not straightforward. We thus present the META algorithm, which employs advanced pruning strategies to effectively reduce the search space. We also design fast techniques to avoid generating duplicated maximal m-clique instances. Our extensive experiments on large real and synthetic HINs show that META is highly effective and efficient.
Persistent Identifierhttp://hdl.handle.net/10722/275197
ISSN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorHu, J-
dc.contributor.authorCheng, CK-
dc.contributor.authorChang, CC-
dc.contributor.authorSankar, A-
dc.contributor.authorFang, Y-
dc.contributor.authorLam, YH-
dc.date.accessioned2019-09-10T02:37:34Z-
dc.date.available2019-09-10T02:37:34Z-
dc.date.issued2019-
dc.identifier.citation35th IEEE International Conference on Data Engineering (ICDE 2019), Macau, China, 8-11 April 2019, p. 746-757-
dc.identifier.issn1084-4627-
dc.identifier.urihttp://hdl.handle.net/10722/275197-
dc.description.abstractWe study the discovery of cliques (or 'complete' subgraphs) in heterogeneous information networks (HINs). Existing clique-finding solutions often ignore the rich semantics of HINs. We propose motif clique, or m-clique, which redefines subgraph completeness with respect to a given motif. A motif, essentially a small subgraph pattern, is a fundamental building block of an HIN. The m-clique concept is general and allows us to analyse 'complete' subgraphs in an HIN with respect to desired high-order connection patterns. We further investigate the maximal m-clique enumeration problem (MMCE), which finds all maximal m-cliques not contained in any other m-cliques. Because MMCE is NP-hard, developing an accurate and efficient solution for MMCE is not straightforward. We thus present the META algorithm, which employs advanced pruning strategies to effectively reduce the search space. We also design fast techniques to avoid generating duplicated maximal m-clique instances. Our extensive experiments on large real and synthetic HINs show that META is highly effective and efficient.-
dc.languageeng-
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000178-
dc.relation.ispartofInternational Conference on Data Engineering. Proceedings-
dc.rightsInternational Conference on Data Engineering. Proceedings. Copyright © IEEE Computer Society.-
dc.rights©2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectClique-
dc.subjectHeterogeneous information networks-
dc.subjectMotif-
dc.titleDiscovering Maximal Motif Cliques in Large Heterogeneous Information Networks-
dc.typeConference_Paper-
dc.identifier.emailCheng, CK: ckcheng@cs.hku.hk-
dc.identifier.authorityCheng, CK=rp00074-
dc.identifier.doi10.1109/ICDE.2019.00072-
dc.identifier.scopuseid_2-s2.0-85067987999-
dc.identifier.hkuros302997-
dc.identifier.spage746-
dc.identifier.epage757-
dc.identifier.isiWOS:000477731600065-
dc.publisher.placeUnited States-
dc.identifier.issnl1084-4627-

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