An approach to merging of two community subgraphs to form a community graph using graph mining techniques

Data mining is known for discovering frequent sub-structures. After finding certain similarity, it is easy to merge the sub-structures to form a larger structure for proper information extraction. To carry out this process, we have proposed new algorithms which merge two community subgraphs in an efficient and simpler way. For our work, we have followed graph matching technique by matching one-to-one correspondence. The three algorithms that have been proposed in this paper are, the first algorithm explains about finding the order of merged communities and to make available of initial form of merged community matrix. The second algorithm explains about creation of adjacency matrix community graph and the third algorithm uses the adjacency matrices of community graph and explains about creation of merged community adjacency matrix. Further, we have verified our proposed approach by implementing it. An appropriate example with the set of input and obtained outputs has been explained. The obtained results are satisfactory. The results were obtained after execution of our programs. Snap-shot of the program output have been included in the paper.