Classification of Approaches and Challenges of Frequent Subgraphs Mining
  in Biological Networks
  Understanding the structure and dynamics of biological networks is one of the
important challenges in system biology. In addition, increasing amount of
experimental data in biological networks necessitate the use of efficient
methods to analyze these huge amounts of data. Such methods require to
recognize common patterns to analyze data. As biological networks can be
modeled by graphs, the problem of common patterns recognition is equivalent
with frequent sub graph mining in a set of graphs. In this paper, at first the
challenges of frequent subgrpahs mining in biological networks are introduced
and the existing approaches are classified for each challenge. then the
algorithms are analyzed on the basis of the type of the approach they apply for
each of the challenges.
