A New Approach of Compression of Large Community Graph Using Graph Mining Techniques

Abstract

Graph representations have vast applications and are used for knowledge extraction. With increase in applications of graph, it has become more and more complex and larger in sizes. Visualization and analyzing large community graph are challenging. To study a large community graph, compression technique may be used. There should not be any loss of information or knowledge while compressing the community graph. This paper starts with a formal introduction followed by representing the graph models in compressed form. Greedy Algorithm is used for the purpose. The paper proceeds in the same direction and proposes a similar technique for compressing a large community graph, which is suitable for carrying out steps of graph mining. Observations show that the proposed technique reduces the iteration steps may leads to a better efficiency. Algorithm on the proposed technique has been elaborated followed by a suitable example.