ViStruclizer: A Structural Visualizer for Multi-dimensional Social Networks

• Main Authors: DAI, Bingtian, Kwee, Agus Trisnajaya, LIM, Ee Peng
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2013
• Subjects: Data Mining and Knowledge Discovery; Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/1893; https://ink.library.smu.edu.sg/context/sis_research/article/2892/viewcontent/ViStruclizer__A_structural_visualizer_for_multi_dimensional_social_networks.pdf
• Institution: Singapore Management University

With the popularity of Web 2.0 sites, social networks today increasingly involve different kinds of relationships among different types of users in a single network. Such social networks are said to be multi-dimensional. Analyzing multi-dimensional networks is a challenging research task that requires intelligent visualization techniques. In this paper, we therefore propose a visual analytics tool called ViStruclizer to analyze structures embedded in a multi-dimensional social network. ViStruclizer incorporates structure analyzers that summarize social networks into both node clusters each representing a set of users, and edge clusters representing relationships between users in the node clusters. ViStruclizer supports user interactions to examine specific clusters of users and inter-cluster relationships, as well as to refine the learnt structural summary.