Mining on graphs has become quiet popular because of the increasing use of graphs in real world applications. Considering the importance of graph applications, the problem of finding frequent itemsets on transactional databases can be transformed to the mining of frequent subgraphs present in a single or set of graphs. The objective of frequent subgraph mining is to extract interesting and meaningful subgraphs which have occurred frequently. The research goals in the discovery of frequent subgraphs are (i) mechanisms that can effectively generate candidate subgraphs excluding duplicates and (ii) mechanisms that find best processing techniques that generate only necessary candidate subgraphs in order to discover the useful and desired frequent subgraphs. In this paper, our prime focus is to give an overview about the state of the art methods in the area of frequent subgraph mining.
S. Thomas and Jyothisha J. Nair, “A Survey on extracting Frequent Subgraphs”, in International Conference on Advances in Computing, Communications and Informatics (ICACCI-2016), 2016.