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Understanding the structure and dynamics of biological networks is one of theimportant challenges in system biology. In addition, increasing amount ofexperimental data in biological networks necessitate the use of efficientmethods to analyze these huge amounts of data. Such methods require torecognize common patterns to analyze data. As biological networks can bemodeled by graphs, the problem of common patterns recognition is equivalentwith frequent sub graph mining in a set of graphs. In this paper, at first thechallenges of frequent subgrpahs mining in biological networks are introducedand the existing approaches are classified for each challenge. then thealgorithms are analyzed on the basis of the type of the approach they apply foreach of the challenges.

Classification of Approaches and Challenges of Frequent Subgraphs Mining in Biological Networks