Discovering parametric clusters in social small-world graphs

We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different research areas, including graph layout, graph clustering and partitioning, machine learning, and user interface design. It helps users explore the networks and develop insights concerning their members and structure that may be difficult or impossible to discover via traditional means, including existing graph visualization and/or statistical methods. Categories and Subject Descriptors I.3.6 [Computer Graphics]: Methodology and Techniques— Interaction Techniques; H.5 [Information Systems]: Information Interfaces and Presentation; I.2.6 [Artificial Intelligence]: Learning Keywords Graph clustering, graph layout, histogram, information visualization, machine learning, self-organizing map, smallworld graph, social networks, user interface design

• Type: Conference
• Year: 2005
• Where: SAC
• Authors: Jonathan McPherson, Kwan-Liu Ma, Michael Ogawa
• Keywords: Applied Computing, Graph Clustering, Graph Layout, SAC 2005, User Interface Design