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A Survey of Clustering Algorithms for Graph Data

  • Charu C. Aggarwal
  • Haixun Wang
Chapter
Part of the Advances in Database Systems book series (ADBS, volume 40)

Abstract

In this chapter, we will provide a survey of clustering algorithms for graph data. We will discuss the different categories of clustering algorithms and recent efforts to design clustering methods for various kinds of graphical data. Clustering algorithms are typically of two types. The first type consists of node clustering algorithms in which we attempt to determine dense regions of the graph based on edge behavior. The second type consists of structural clustering algorithms, in which we attempt to cluster the different graphs based on overall structural behavior. We will also discuss the applicability of the approach to other kinds of data such as semi-structured data, and the utility of graph mining algorithms to such representations.

Keywords

Graph Clustering Dense Subgraph Discovery 

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Copyright information

© Springer-Verlag US 2010

Authors and Affiliations

  1. 1.IBM T. J. Watson Research CenterHawthorneUSA
  2. 2.Microsoft Research AsiaBeijingChina

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