https://scholars.lib.ntu.edu.tw/handle/123456789/489831
Title: | Centrality Analysis, Role-Based Clustering, and Egocentric Abstraction for Heterogeneous Social Networks. | Authors: | Li, Cheng-Te SHOU-DE LIN |
Issue Date: | 2012 | Start page/Pages: | 1-10 | Source: | 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 | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/489831 | DOI: | 10.1109/SocialCom-PASSAT.2012.59 | SDG/Keyword: | abstraction; centrality; clustering; Heterogeneous information; Social Networks; Abstracting; Data structures; Heterogeneous networks; Information services; Semantics; Social networking (online) |
Appears in Collections: | 資訊工程學系 |
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