Detection of Sub-Community Graph in N-Community Graphs using Graph Mining
Bapuji Rao1, Sarojananda Mishra2
1Bapuji Rao*, Department of CSE, Biju Patnaik University of Technology, Rourkela, India.
2Sarojananda Mishra, Department of CSEA, Indira Gandhi Institute of Technology, Sarang, India.
Manuscript received on February 05, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2014-2023 | Volume-9 Issue-3, February 2020. | Retrieval Number: B4530129219/2020©BEIESP | DOI: 10.35940/ijeat.B4530.029320
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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: Detection of sub-graphs in community graphs is an important task and useful for characterizing community graphs. This characterization leads to classification as well as clusterings of community graphs. It also leads to finding differences among a set of community graphs as well as buildings of indices of community graphs. Finally, this characterization leads discovery of knowledge from sub-graphs. This proposed approach of detection of a sub-community graph from a group of community graphs using simple graph theory techniques. So, that knowledge could be discovered from the sub-community graph detected in a set of community graphs. The proposed algorithm has been implemented with two examples including one benchmark network and observed satisfactory results.
Keywords: community graph, community adjacency matrix, sub-community adjacency matrix, sub-community graph.