Frequent subgraph mining algorithms for single large graphs — A brief survey

Modeling data as a graph has proved an efficient approach considering huge amount of data and large number of relationships among them. The process of mining data sets, represented as graph structures, called graph mining is widely studied in bioinformatics, computer networks, chemical reactions, social networks, program flow structures, etc. Frequent Subgraph Mining is defined as finding all the subgraphs in a given graph that appear more number of times than a given value. Frequent subgraph mining process for single large graph consists of three phases, i.e., candidate generation, support computation and result generation and sets of techniques used in each phase. This paper provides a brief survey of the frequent subgraph mining algorithms focusing on the type of techniques they use in the algorithm in respective phases.