A comparative survey of algorithms for frequent subgraph discovery

Graph mining is a well-explored area of research where frequent subgraph discovery is an important problem. To get an understanding of various frequent subgraph discovery algorithms and to assess their suitability to various application scenarios, it is important to establish a common framework for their study. The present article addresses this need by bringing out a classification scheme emphasizing the intrinsic characteristics of these algorithms. The classification scheme is based on the search strategy, the nature of the input, and the completeness of the output of these algorithms. A short discussion on a few more recent algorithms is also included. An experimental evaluation that explores the relevance and applicability of a subset of these algorithms for some current application scenarios is furnished for completeness.

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