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A New Approach to Community Graph Partition Using Graph Mining Techniques

A New Approach to Community Graph Partition Using Graph Mining Techniques

Bapuji Rao, Sarojananda Mishra
Copyright: © 2017 |Volume: 4 |Issue: 1 |Pages: 20
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522515715|DOI: 10.4018/IJRSDA.2017010105
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MLA

Rao, Bapuji, and Sarojananda Mishra. "A New Approach to Community Graph Partition Using Graph Mining Techniques." IJRSDA vol.4, no.1 2017: pp.75-94. http://doi.org/10.4018/IJRSDA.2017010105

APA

Rao, B. & Mishra, S. (2017). A New Approach to Community Graph Partition Using Graph Mining Techniques. International Journal of Rough Sets and Data Analysis (IJRSDA), 4(1), 75-94. http://doi.org/10.4018/IJRSDA.2017010105

Chicago

Rao, Bapuji, and Sarojananda Mishra. "A New Approach to Community Graph Partition Using Graph Mining Techniques," International Journal of Rough Sets and Data Analysis (IJRSDA) 4, no.1: 75-94. http://doi.org/10.4018/IJRSDA.2017010105

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Abstract

Knowledge extraction is very much possible from the community graph using graph mining techniques. The authors have studied the related definitions of graph partition in terms of both mathematical as well as computational aspects. To derive knowledge from a particular sub-community graph of a large community graph, the authors start partitioning the large community graph into smaller sub-community graphs. Thus, the knowledge extraction from the sub-community graph becomes easier and faster. The proposed approach of partition is done by detection of edges among the community members of dissimilar community. By studying existing techniques followed by different researchers, the authors propose a new and simple algorithm for partitioning the community graph into sub-community graphs using graph mining techniques. Finally, the authors have considered a benchmark dataset as example which verifies the strength and easiness of the proposed algorithm.

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