Multi-view clustering for mining heterogeneous social network data

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Title: Multi-view clustering for mining heterogeneous social network data
Authors: Greene, Derek
Cunningham, Pádraig
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Date: Mar-2009
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: Conference Publication
Keywords: Social network analysisMachine learningBibliometrics
Subject LCSH: Social networks
Machine learning
Cluster analysis
Language: en
Status of Item: Not peer reviewed
Conference Details: 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
Appears in Collections:Computer Science Research Collection

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