CSV: visualizing and mining cohesive subgraphs

Extracting dense sub-components from graphs efficiently is an important objective in a wide range of application domains ranging from social network analysis to biological network analysis, from the World Wide Web to stock market analysis. Motivated by this need recently we have seen several new algorithms to tackle this problem based on the (frequent) pattern mining paradigm. A limitation of most of these methods is that they are highly sensitive to parameter settings, rely on exhaustive enumeration with exponential time complexity, and often fail to help the users understand the underlying distribution of components embedded within the host graph. In this article we propose an approximate algorithm, to mine and visualize cohesive subgraphs (dense sub components) within a large graph. The approach, refereed to as Cohesive Subgraph Visualization (CSV) relies on a novel mapping strategy that maps edges and nodes to a multidimensional space wherein dense areas in the mapped space corres...

• Type: Conference
• Year: 2008
• Where: SIGMOD
• Authors: Nan Wang, Srinivasan Parthasarathy, Kian-Lee Tan, Anthony K. H. Tung
• Keywords: Cohesive Subgraph Visualization, Cohesive Subgraphs, Database, SIGMOD 2008, Subgraph Mining Algorithms