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Identification of Community Structure in Networks

Authors: Jun Chen; Tieju Ma; Chunjie Chi;

Identification of Community Structure in Networks

Abstract

In recent years, researchers show considerable interests in identification of communities in complex networks. Diverse methods have been developed to address this problem. This paper presents a new algorithm for identifying communities in complex networks. The algorithm was inspired by studies on the effect of structural holes in networks. We tested the performance of the new algorithm with several complex networks and the test show the algorithm efficient.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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