Community mining from multi-relational networks

Authors:
Deng Cai;Zheng Shao;Xiaofei He;Xifeng Yan;Jiawei Han
Affiliations:
Computer Science Department, University of Illinois at Urbana Champaign;Computer Science Department, University of Illinois at Urbana Champaign;Computer Science Department, University of Chicago;Computer Science Department, University of Illinois at Urbana Champaign;Computer Science Department, University of Illinois at Urbana Champaign
Venue:
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2005

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Abstract

Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the network, and moreover, the mining results are independent of the users’ needs or preferences. However, in reality, there exist multiple, heterogeneous social networks, each representing a particular kind of relationship, and each kind of relationship may play a distinct role in a particular task. In this paper, we systematically analyze the problem of mining hidden communities on heterogeneous social networks. Based on the observation that different relations have different importance with respect to a certain query, we propose a new method for learning an optimal linear combination of these relations which can best meet the user’s expectation. With the obtained relation, better performance can be achieved for community mining.