Abstract:
In this paper, we propose a new approach to the problem of subgraph isomorphism detection. The new method is designed for systems which differentiate between graphs that are a priori known, so-called model graphs, and unknown graphs, so-called input graphs. The problem to be solved is to find a subgraph isomorphism from an input graph, which is given on-line, to any of the model graphs. The new method is based on an intensive preprocessing step in which the model graphs are used to create a decision tree. At run time, the input graph is then classified by the decision tree and all model graphs for which there exists a subgraph isomorphism from the input graph are detected. If we neglect the time needed for preprocessing, the computational complexity of the new subgraph isomorphism algorithm is only quadratic in the number of input graph vertices. Furthermore, it is independent of the number of model graphs and the number of edges in any of the graphs. However, the decision tree that i...
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