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Abstract

Representation of any network graphically has vast applications and used for knowledge extraction efficiently. Due to the increase in applications of a graph, the size of the graph becomes larger as well as its complexity becomes more and more. So visualization and analyzing of a large community graph are more challenging. Hence compression technique may be used to study a large community graph for knowledge extraction. During compression, there should not be any loss of information. This paper proposes an algorithm, “ComComGra” which compresses a large community graph with various communities using graph mining techniques. The proposed algorithm elaborates with two examples which include a benchmark example.

Keywords

Community community graph community members compressed community graph self-edge

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