Bibliometric approach to community discovery
N Deo, H Balakrishnan - Proceedings of the 43rd annual Southeast …, 2005 - dl.acm.org
Proceedings of the 43rd annual Southeast regional conference-Volume 2, 2005•dl.acm.org
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.
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.
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.
ACM Digital LibraryShowing the best result for this search. See all results