Managing and Mining Graph Data part 30 is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. . | Chapter 9 A SURVEY OF CLUSTERING ALGORITHMS IFOR GRAPH DATA Chani 2. Aggarwal IBM T. J. Watson Research Center Hawthorne NY 10532 charu@ Ifaixun Wang Microsoft Research Asia Beijing China 100190 haixunw@ Abstract In this chapter we will provide a survey of clustering algorithms for graph data. We will discuss the difiereni categories of clustering algorithms and recent efforts to design clustersng methods for various kinds of graphical data. Clustering algosithms iii e typicslIy of two typcSi The hist type consists of node clustering afgosithms m which we attempt So determine druse regions of the graph based on edye huhavior. The sect md type consisls of structural clustering algorithms in whiyh we allempl So cluster she dii ierenl graphs based on overall structural behavior USce will als e discuss She applicebiltty of She approach to other kinds of data such ac scmi-ststiclttsed sttnrii and the siiilily of graph mining algorithms to such representations. Keywords Graph Clustering Dense Subgraph Discovery 1. Introduction Graph mining iit iM been sc popul tsr area srC research in recent years because if numerous in ci biology software bug localization aed computer neSwotissngi Sn addition manor new kinds of data such as semi- . Aggarwal and H. Wang eds. Managing and Mining Graph Data Advances in Database Systems 40 DOI 978-1-4419-6045-0_9 Springer Science Business Media LLC 2010 275 2776 MANAGING AND MINING GRAPH DATA data and XMI. 2 can typically he represented as graphs. In particular XML data ts a popular representation of different kinds of data sets. Since core graph-mining algorithma can be extended to this scenario it follows that the extension of mining algorithmr to graphs hai tremendous applicability of a wide varieti of daia rets whnnh are reprereniad as semi-structured data. Many iraditianal algorithms tuch as ctoiiitt ria g. clarsification and frequent-pattern mitring have teca
