GraphClust: A Method for Clustering Database of Graphs

Diego Reforgiato (), Rodrigo Gutierrez () and Dennis Shasha ()
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Diego Reforgiato: Dipartimento di Matematica e Informatica, Università degli Studi di Catania, Italy
Rodrigo Gutierrez: Biology Department, New York University, USA;
Dennis Shasha: Computer Science Department, New York University, USA

Journal of Information & Knowledge Management (JIKM), 2008, vol. 07, issue 04, 231-241

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.

Keywords: Text clustering; document vectors; graph clustering; graph substructure matching (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1142/S0219649208002093

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