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GraphClust: A Method for Clustering Database of Graphs

Author

Listed:
  • Diego Reforgiato

    (Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Italy)

  • Rodrigo Gutierrez

    (Biology Department, New York University, USA;
    Departamento de Genetica Molecular y Microbiologia, P. Universidad Catolica, Santiago, Chile)

  • Dennis Shasha

    (Computer Science Department, New York University, USA)

Abstract

Any application that represents data as sets of graphs may benefit from the discovery of relationships among those graphs. To do this in an unsupervised fashion requires the ability to find graphs that are similar to one another. That is the purpose of GraphClust. The GraphClust algorithm proceeds in three phases, often building on other tools:(1) it finds highly connected substructures in each graph;(2) it uses those substructures to represent each graph as a feature vector; and(3) it clusters these feature vectors using a standard distance measure. We validate the cluster quality by using the Silhouette method. In addition to clustering graphs, GraphClust uses SVD decomposition to find frequently co-occurring connected substructures. The main novelty of GraphClust compared to previous methods is that it is application-independent and scalable to many large graphs.

Suggested Citation

  • Diego Reforgiato & Rodrigo Gutierrez & Dennis Shasha, 2008. "GraphClust: A Method for Clustering Database of Graphs," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 231-241.
    • Handle: RePEc:wsi:jikmxx:v:07:y:2008:i:04:n:s0219649208002093
    DOI: 10.1142/S0219649208002093
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