Abstract：
:Detecting communities or clusters in a real-world, networked system is of considerable interest in various fields such as sociology, biology, physics, engineering science, and interdisciplinary subjects, with significant efforts devoted in recent years. Many existing algorithms are only designed to identify the composition of communities, but not the structures. Whereas we believe that the local structures of communities can also shed important light on their detection. In this work, we develop a simple yet effective approach that simultaneously uncovers communities and their centers. The idea is based on the premise that organization of a community generally can be viewed as a high-density node surrounded by neighbors with lower densities, and community centers reside far apart from each other. We propose so-called "community centrality" to quantify likelihood of a node being the community centers in such a landscape, and then propagate multiple, significant center likelihood throughout the network via a diffusion process. Our approach is an efficient linear algorithm, and has demonstrated superior performance on a wide spectrum of synthetic and real world networks especially those with sparse connections amongst the community centers.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Chen Y,Zhao P,Li P,Zhang K,Zhang Jdoi
10.1038/srep24017subject
Has Abstractpub_date
2016-04-07 00:00:00pages
24017issn
2045-2322pii
srep24017journal_volume
6pub_type
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