Complete graph analysis in community detection
By: Han, Lihong.
Contributor(s): Zhou, Qingguo.
Publisher: Haryana IOSR - International Organization of Scientific Research 2022Edition: Vol.24(3), May-Jun.Description: 11-14p.Subject(s): Computer EngineeringOnline resources: Click here In: IOSR Journal of Computer Engineering (IOSR-JCE)Summary: Community detection in graphs identifies groups and is an essential component of graph theory. The clique percolation method (CPM) has been widely used in graph analysis, but there are computation issues when graphs are large. In this study, we use a Venture Capital dataset from 50 years and show the limitations of the k-clique algorithms. Alternatively, we conducted a complete subgraph search for community detection. The computation time and performance of our complete subgraph search are significantly better than the k-clique algorithm.Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database | School of Engineering & Technology Archieval Section | Not for loan | 2022-2105 |
Community detection in graphs identifies groups and is an essential component of graph theory. The clique
percolation method (CPM) has been widely used in graph analysis, but there are computation issues when
graphs are large. In this study, we use a Venture Capital dataset from 50 years and show the limitations of the
k-clique algorithms. Alternatively, we conducted a complete subgraph search for community detection. The
computation time and performance of our complete subgraph search are significantly better than the k-clique
algorithm.
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