Affiliations: Department of Computer Science Engineering and Applications, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
Correspondence:
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Corresponding author: Bapuji Rao, Department of Computer Science Engineering and Applications, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India E-mail: bapuji.research@gmail.com.
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
