Multi-view clustering for mining heterogeneous social network data

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Show simple item record Greene, Derek Cunningham, Pádraig 2010-03-29T14:15:08Z 2010-03-29T14:15:08Z 2009-03
dc.description Paper presented at the Workshop on Information Retrieval over Social Networks, 31st European Conference on Information Retrieval (ECIR'09), Toulouse, France, April 6-9, 2009 en
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland en
dc.description.uri Conference details en
dc.description.uri en
dc.format.extent 346075 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.subject Social network analysis en
dc.subject Machine learning en
dc.subject Bibliometrics en
dc.subject.lcsh Social networks en
dc.subject.lcsh Machine learning en
dc.subject.lcsh Cluster analysis en
dc.subject.lcsh Bibliometrics en
dc.title Multi-view clustering for mining heterogeneous social network data en
dc.type Conference Publication en
dc.internal.availability Full text available en
dc.status Not peer reviewed en
dc.neeo.contributor Greene|Derek|aut| en
dc.neeo.contributor Cunningham|Pádraig|aut| en

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