In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.

Salim Jouili, and Salvatore Tabbone, Hypergraph-based image retrieval for graph-based representation. Journal of the Pattern Recognition Society, April 2012. © 2012 Elsevier Ltd.

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