#5903. CGraM: Enhanced algorithm for community detection in social networks

July 2026publication date
Proposal available till 19-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Geometry and Topology;
Theoretical Computer Science;
Software;
Places in the authors’ list:
place 1place 2place 3place 4
FreeFreeFreeFree
2350 $1200 $1050 $900 $
Contract №5903.1 Contract №5903.2 Contract №5903.3 Contract №5903.4
1 place - free (for sale)
2 place - free (for sale)
3 place - free (for sale)
4 place - free (for sale)

More details about the manuscript: Science Citation Index Expanded or/and Social Sciences Citation Index
Abstract:
Community Detection is used to discover a non-trivial organization of the network and to extract the special relations among the nodes which can help in understanding the structure and the function of the networks. However, community detection in social networks is a vast and challenging task, in terms of detected communities accuracy and computational overheads. In this paper, we propose a new algorithm Enhanced Algorithm for Community Detection in Social Networks – CGraM, for community detection using the graph measures eccentricity, harmonic centrality and modularity. First, the centre nodes are identified by using the eccentricity and harmonic centrality, next a preliminary community structure is formed by finding the similar nodes using the jaccard coefficient. Later communities are selected from the preliminary community structure based on the number of inter-community and intra-community edges between them. Then the selected communities are merged till the modularity improves to form the better resultant community structure.
Keywords:
Centrality; Community detection; Eccentricity; Jaccard co-efficient; Modularity; Social network

Contacts :
0