Multi-View Clustering for Mining Heterogeneous Social Network Data
|Title:||Multi-View Clustering for Mining Heterogeneous Social Network Data||Authors:||Greene, Derek; Cunningham, Pádraig||Permanent link:||http://hdl.handle.net/10197/12379||Date:||Mar-2009||Online since:||2021-08-05T14:04:24Z||Abstract:||Uncovering community structure is a core challenge in social network analysis. This is a significant challenge for large networks where there is a single type of relation in the network (e.g. friend or knows). In practice there may be other types of relation, for instance demographic or geographic information, that also reveal network structure. Uncovering structure in such multi-relational networks presents a greater challenge due to the difficulty of integrating information from different, often discordant views. In this paper we describe a system for performing cluster analysis on heterogeneous multi-view data, and present an analysis of the research themes in a bibliographic literature network, based on the integration of both co-citation links and text similarity relationships between papers in the network.||Funding Details:||Science Foundation Ireland||Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD CSI Technical Reports; ucd-csi-2009-4||Copyright (published version):||2009 the Authors||Keywords:||Case-based reasoning; Parallel integration clustering algorithm (PICA); Social networks; Analysis tasks||Other versions:||https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||CLARITY Research Collection|
CASL Research Collection
Computer Science and Informatics Technical Reports
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