Abstract
Data mining to derive frequent subgraphs from a dataset of general graphs has high computational complexity because it includes the explosively combinatorial search for candidate subgraphs and subgraph isomorphism matching. Although some approaches have been proposed to derive characteristic patterns from graph structured data, they limit the graphs to be searched within a specific class. In this paper, we propose an approach to conduct a complete search of various classes of frequent subgraphs in a massive dataset of labeled graphs within practical time. The power of our approach comes from the algebraic representation of graphs, its associated operations and well-organized bias constraints to limit the search space efficiently. Its performance has been evaluated through real world datasets, and the high scalability of our approach has been confirmed with respect to the amount of data and the computation time.
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