Local Community Identification in Social Networks

There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have bee...

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Bibliographic Details
Published in2009 International Conference on Advances in Social Network Analysis and Mining : 20-22 July 2009 pp. 237 - 242
Main Authors Jiyang Chen, Zaiane, O., Goebel, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Accuracy
Communities
Community Mining
Data mining
Educational institutions
Image edge detection
Local Community
Social Network
Social network services
World Wide Web
Online AccessGet full text
ISBN9780769536897
0769536891
DOI10.1109/ASONAM.2009.14

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Summary:There has been much recent research on identifying global community structure in networks. However, most existing approaches require complete information of the graph in question, which is impractical for some networks, e.g. the World Wide Web (WWW). Algorithms for local community detection have been proposed but their results usually contain many outliers. In this paper, we propose a new measure of local community structure, coupled with a two-phase algorithm that extracts all possible candidates first, and then optimizes the community hierarchy. We compare our results with previous methods on real world networks such as the co-purchase network from Amazon. Experimental results verify the feasibility and effectiveness of our approach.
ISBN:9780769536897
0769536891
DOI:10.1109/ASONAM.2009.14