Graph Clustering

  • Chris Biemann
Part of the Theory and Applications of Natural Language Processing book series (NLP)


This chapter is devoted to discovering structure in graphs by grouping their vertices together in meaningful clusters. In Section 4.1, clustering in its broadness is briefly reviewed in general. The discipline of graph clustering is embedded into the broad field of clustering and discussed in detail and a variety of graph clustering algorithms are examined in terms of mechanism, algorithmic complexity and adequacy for scale-free SmallWorld graphs. Taking their virtues and drawbacks into consideration, an efficient graph partitioning algorithm called Chinese Whispers is developed in Section 4.2. It is time-linear in the number of edges, finds the number of clusters automatically and does not impose relative size restrictions on clusters, which is adequate for graphs from language data. Several extensions and parametrisations of the method are discussed. This algorithm will be used throughout later chapters to solve several NLP tasks.


Edge Weight Normalise Mutual Information Large Graph Very Large Scale Integration Graph Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Chris Biemann
    • 1
  1. 1.Computer Science DepartmentTechnische Universität DarmstadtDarmstadtGermany

Personalised recommendations