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 |
|---|---|---|---|---|---|---|
Articles Abstract Database
|
School of Engineering & Technology Archieval Section | Not for loan | 2022-2105 |
Total holds: 0
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.
Click on an image to view it in the image viewer
There are no comments for this item.