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, DerekCunningham, Pádraig
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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 reasoningParallel integration clustering algorithm (PICA)Social networksAnalysis tasks
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Language: en
Status of Item: Not peer reviewed
This item is made available under a Creative Commons License:
Appears in Collections:CLARITY Research Collection
CASL Research Collection
Computer Science and Informatics Technical Reports

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