Defining least community as a homogeneous group in complex networks
2015 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 428, 154-160 p.Article in journal (Refereed) Published
This paper introduces a new concept of least community that is as homogeneous as a random graph, and develops a new community detection algorithm from the perspective of homogeneity or heterogeneity. Based on this concept, we adopt head/tail breaks-a newly developed classification scheme for data with a heavy-tailed distribution-and rely on edge betweenness given its heavy-tailed distribution to iteratively partition a network into many heterogeneous and homogeneous communities. Surprisingly, the derived communities for any self-organized and/or self-evolved large networks demonstrate very striking power laws, implying that there are far more small communities than large ones. This notion of far more small things than large ones constitutes a new fundamental way of thinking for community detection. © 2015 Elsevier B.V. All rights reserved.
Place, publisher, year, edition, pages
2015. Vol. 428, 154-160 p.
Classification, Head/tail breaks, ht-index, k-means, Natural breaks, Scaling, Classification (of information), Iterative methods, Population dynamics, Complex networks
Computer and Information Science
IdentifiersURN: urn:nbn:se:hig:diva-19212DOI: 10.1016/j.physa.2015.02.029ISI: 000352328100015ScopusID: 2-s2.0-84923791049OAI: oai:DiVA.org:hig-19212DiVA: diva2:805902