Fast algorithm for detecting community structure in networks.
TLDR
An algorithm is described which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms.Abstract:
Many networks display community structure--groups of vertices within which connections are dense but between which they are sparser--and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50,000 physicists.read more
Citations
More filters
Journal ArticleDOI
Synchronization and modularity in complex networks
Alex Arenas,Albert Díaz-Guilera +1 more
TL;DR: Simulating the Kuramoto's model in complex networks and finding patterns of meta-stability indicate that the more stable the patterns are, the larger tends to be the modularity of the partition defined by them.
Journal ArticleDOI
Fundamental statistical features and self-similar properties of tagged networks
TL;DR: This work investigates the fundamental statistical features of tagged (or annotated) networks having a rich variety of attributes associated with their nodes and introduces a number of new notions, including tag-assortativity (relating link probability to node similarity), and new quantities, such as node uniqueness and tag-Assortativity exponent.
Journal ArticleDOI
Finding linkage between technology and social issue: A Literature Based Discovery approach
TL;DR: In this article, the authors investigated Literature Based Discovery (LBD) approach to reveal linkages between technology and social issue to elucidate plausible contribution of science and technology for solving social issues.
Journal ArticleDOI
The influence of floral traits on specialization and modularity of plant-pollinator networks in a biodiversity hotspot in the Peruvian Andes.
TL;DR: The findings underline that specialization indices convey different concepts of specialization and hence quantify different aspects, and that measuring specialization requires careful consideration of what defines a specialist.
Journal ArticleDOI
Patient-Sharing Networks of Physicians and Health Care Utilization and Spending Among Medicare Beneficiaries
Bruce E. Landon,Bruce E. Landon,Nancy L. Keating,Nancy L. Keating,Jukka-Pekka Onnela,Alan M. Zaslavsky,Nicholas A. Christakis,A. James O'Malley +7 more
TL;DR: Characteristics of physicians’ networks and the position of physicians in the network were associated with overall spending and utilization of services for Medicare beneficiaries and the various measures of quality were inconsistently related to the network measures.
References
More filters
疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A
TL;DR: PfPMP1）与感染红细胞、树突状组胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作�ly.
Journal ArticleDOI
Statistical mechanics of complex networks
TL;DR: In this paper, a simple model based on the power-law degree distribution of real networks was proposed, which was able to reproduce the power law degree distribution in real networks and to capture the evolution of networks, not just their static topology.
Journal ArticleDOI
The Structure and Function of Complex Networks
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Journal ArticleDOI
Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
Journal ArticleDOI
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.