Sub-Community Graph Retrieval from a Compressed Community Graph using Graph Mining
Bapuji Rao1, Sarojananda Mishra2
1Bapuji Rao*, CSE, Biju Patnaik University of Technology, Rourkela, India.
2Sarojananda Mishra, CSEA, Indira Gandhi Institute of Technology, Sarang, India.
Manuscript received on September 15, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 6176-6182 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1660109119/2019©BEIESP | DOI: 10.35940/ijeat.A1660.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Community detection and its retrieval is one of the most relevant and important topics in graph mining. Hence it is treated as one of the important applications in the field of social network analysis. Community detection plays an important role in a large community graph by enabling and selecting the desired community’s sub-graph. The proposed algorithm detects and extracts the desired sub-community graph from a compressed community graph for further analysis purpose. The authors present both theoretical and experimental results with three benchmark social networks. The proposed technique is efficient in terms of complexities.
Keywords: Adjacency community matrix, Community graph, Sub-community graph, Self-loop or cycle, Weights.