A Database of Graphs for Isomorphism and Sub-Graph Isomorphism Benchmarking P.Foggia, C.Sansone, M.Vento University of Naples, "Federico II" Via Claudio 21, I-80125 Napoli (Italy) E-mail:{foggiapa,carlosan,vento}@unina.it Abstract Despite of the fact that graph based methods are gaining more and more popularity in different scientific areas, it has to be considered that the choice of an appropriate algorithm for a given application is still the most crucial task. The lack of a wide database of graphs make it difficult the task of comparing the performances of different graph matching algorithms, and often the selection of an algorithm is made on the basis of a few experimental data available on it. In this paper we describe a database containing 72,800 couples of graphs especially devised for comparing the performance of isomorphism and graph-subgraph isomorphism algorithms. The 72,800 couples are split into 18,200 couples of isomorphic graphs and 54,600 couples of graphs with a sub-graph isomorphism mapping among them. The graphs include different categories as Randomly Connected Graphs, Regular Meshes (2D, 3D and 4D), Bounded Valence Graphs, Irregular Meshes and Irregular Bounded Valence Graphs. The size of the graphs ranges from few dozens to about 1000 nodes. The database has been used for a benchmarking activity of isomorphism algorithms [1]. Introduction Graphs are data structures endowed with such an expressive power to make their use profitable in the most disparate areas. By attributing a suitable meaning to nodes and branches of a graph, it is possible to achieve complete and univocal representations, of interest within many application domains, ranging from scientific to humanistic areas. Just to cite a few examples, graphs have been profitably used for representing syntactic components and their relationships in the sentences of a language [2], so as for describing the structure of chemical compounds or for dealing with traffic problems [3]. Recently, graphs have excited more and more interest within the scientific community active in the fields of Pattern Recognition and Computer Vision, and the applications employing graphs have multiplied [4-6].