Multi-view clustering for mining heterogeneous social network data
|Title:||Multi-view clustering for mining heterogeneous social network data||Authors:||Greene, Derek
|Permanent link:||http://hdl.handle.net/10197/1891||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 analysis; Machine learning; Bibliometrics||Subject LCSH:||Social networks
|Language:||en||Status of Item:||Not peer reviewed||Conference Details:||Paper presented at the Workshop on Information Retrieval over Social Networks, 31st European Conference on Information Retrieval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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