Title

Bibliometric Approach To Community Discovery

Keywords

Community discovery/identification; Graph clustering

Abstract

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. Copyright 2005 ACM.

Publication Date

12-1-2005

Publication Title

Proceedings of the Annual Southeast Conference

Volume

2

Number of Pages

241-242

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/1167253.1167264

Socpus ID

77953796049 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953796049

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