✦ LIBER ✦

The art of community detection

✍ Scribed by Natali Gulbahce; Sune Lehmann

Publisher: John Wiley and Sons
Year: 2008
Tongue: English
Weight: 170 KB
Volume: 30
Category: Article
ISSN: 0265-9247
DOI: 10.1002/bies.20820

No coin nor oath required. For personal study only.

✦ Synopsis

Abstract

Networks in nature possess a remarkable amount of structure. Via a series of data‐driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman,1 introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection. BioEssays 30:934–938, 2008. © 2008 Wiley Periodicals, Inc.

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