Bibliometric approach to community discovery

Recent research suggests that most of the real-world random networks organize themselves into communities. Communities are formed by subsets of nodes in a graph, which are closely related. Extracting these communities would lead to a better understanding of such networks. In this paper we propose a novel approach to discover communities using bibliographic metrics, and test the proposed algorithm on real-world networks as well as with computer-generated models with known community structure. Categories and Subject Descriptors G.2.2 [Discrete Mathematics]: Graph Theory—graph algorithms, network problems. General Terms Algorithms. Keywords Community discovery/identification, graph clustering.

• Type: Conference
• Year: 2005
• Where: ACMSE
• Authors: Narsingh Deo, Hemant Balakrishnan
• Keywords: ACMSE 2005, General Terms Algorithms, Real-world Networks, Real-world Random Networks, Theoretical Computer Science