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
Automated Sub-Zoning of Water Distribution Systems
TL;DR: This work presents a generic framework for improved analysis and management of WDS by partitioning the system into smaller (almost) independent sub-systems with balanced loads and minimal number of interconnections.
Journal ArticleDOI
Emotional community detection in social networks
TL;DR: The need for an efficient and innovative methodology for community detection that will also leverage users’ behavior on emotional level is addressed and substantial evidence indicates that the proposed methodology creates influential enough communities.
Journal ArticleDOI
Network-theoretic approach to sparsified discrete vortex dynamics
Aditya G. Nair,Kunihiko Taira +1 more
TL;DR: The sparsified-dynamics model developed with spectral graph theory requires a reduced number of vortex-to-vortex interactions but agrees well with the full nonlinear dynamics and conserves the invariants of discrete vortex dynamics.
Journal ArticleDOI
Community Detection in Complex Networks via Clique Conductance.
TL;DR: This paper develops a novel community-detection method based on cliques, i.e., local complete subnetworks, and shows that the proposed method is guaranteed to detect near-optimal clusters in the bipartition case.
Journal ArticleDOI
Functional atlas of the awake rat brain: A neuroimaging study of rat brain specialization and integration
TL;DR: These functional parcellations reveal the regional specialization of the rat brain, which exhibited high within‐parcel homogeneity and high reproducibility across animals, and provides compelling evidence that the cingulate cortex is a functional hub region conserved from rodents to humans.
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.