In recent years, there exist many large graphs available including social networks, sensor networks, biological networks, etc. Such large graphs need to be fully investigated. Graph clustering has shown its effectiveness in analyzing and visualizing large graphs. The goal of clustering a large graph is to partition vertices into different clusters based on various criteria. Almost all existing graph clustering approaches focus on either the similarity of topological structure around vertices or the similarity of attribute values associated with vertices. In this talk, we discuss a new graph clustering approach based on both structural and attribute similarities using a unified distance measure. Our approach partitions a large attribute graph into k clusters such that each cluster contains a densely connected subgraph with homogeneous attribute values. We will discuss the details of our approach in this talk.
Short Biography:
Dr Jeffrey Xu Yu is a Professor in the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong. His current main research interests include graph mining, graph query processing, graph pattern matching, and keywords search in relational databases. Dr. Yu served/serves in over 200 organization committees and program committees in international conferences/workshops. Dr. Yu also served as an associate editor of IEEE Transactions on Knowledge and Data Engineering (2004-2008), and currently servers in VLDB Journal editorial board and ACM SIGMOD executive committee. He has published over 200 papers including papers published in reputed journals and major international conferences. |