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The art of community detection
Author(s) -
Gulbahce Natali,
Lehmann Sune
Publication year - 2008
Publication title -
bioessays
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.175
H-Index - 184
eISSN - 1521-1878
pISSN - 0265-9247
DOI - 10.1002/bies.20820
Subject(s) - computer science , complex network , hierarchy , community structure , identification (biology) , theoretical computer science , network theory , network science , random graph , graph , graph theory , series (stratigraphy) , data science , artificial intelligence , mathematics , biology , world wide web , ecology , statistics , combinatorics , economics , paleontology , market economy
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.