Egocentric Information Abstraction for Heterogeneous Social Networks

• Published in: 2009 International Conference on Advances in Social Network Analysis and Mining : 20-22 July 2009 pp. 255 - 260
• Main Authors: Cheng-Te Li, Shou-De Lin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2009
• Subjects: Abstraction; Data mining; Egocentric; Feature extraction; graph mining; Heterogeneous Network; Humans; Industries; Motion pictures; Social Network; Social network services; Visualization
• ISBN: 9780769536897; 0769536891
• DOI: 10.1109/ASONAM.2009.38

Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify linear combination of relations as features and compute statistical dependencies as feature values. Then we design several abstraction criteria to distill representative and important information to construct the abstracted graphs for visualization. The evaluations conducted on a real world movie dataset and an artificial crime dataset demonstrate that the abstractions can indeed retain important information and facilitate more accurate and efficient human analysis.