Centrality Analysis, Role-Based Clustering, and Egocentric Abstraction for Heterogeneous Social Networks.
Journal
2012 International Conference on Privacy, Security, Risk and Trust, PASSAT 2012, and 2012 International Confernece on Social Computing, SocialCom 2012, Amsterdam, Netherlands, September 3-5, 2012
Pages
1-10
Date Issued
2012
Author(s)
Li, Cheng-Te
Abstract
The social network is a powerful data structure allowing the depiction of relationship information between entities. Recent researchers have proposed many successful methods on analyzing homogeneous social networks assuming only a single type of node and relation. Nevertheless, real-world complex networks are usually heterogeneous, which presumes a network can be composed of different types of nodes and relations. In this paper, we propose an unsupervised tensor-based mechanism considering higher-order relational information to model the complex semantics of a heterogeneous social network. Based on the model we present solutions to three critical issues in heterogeneous networks. The first concerns identifying central nodes in the heterogeneous network. Second, we propose a role-based clustering method to identify nodes which play similar roles in the network. Finally, we propose an egocentric abstraction mechanism to facilitate further explorations in a complex social network. The evaluations are conducted on a real-world movie dataset and an artificial crime dataset with promising results. © 2012 IEEE.
SDGs
Other Subjects
abstraction; centrality; clustering; Heterogeneous information; Social Networks; Abstracting; Data structures; Heterogeneous networks; Information services; Semantics; Social networking (online)
Type
conference paper