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
The emergence of unshared consensus decisions in bottlenose dolphins
David Lusseau,Larissa Conradt +1 more
TL;DR: While side flops were performed by males that have greater knowledge than other male group members, this was not the case for females performing upside-down lobtails, and it could have been that a generally high knowledge about the optimal timing of travel terminations rendered it less important which individual female made the decision.
Proceedings ArticleDOI
Object Mining Using a Matching Graph on Very Large Image Collections
James Philbin,Andrew Zisserman +1 more
TL;DR: This work focuses on grouping images containing the same object, despite significant changes in scale, viewpoint and partial occlusions, in very large image collections automatically gathered from Flicker, the largest dataset to which image-based data mining has been applied.
Book
Handbook of research on methods and techniques for studying virtual communities : paradigms and phenomena
TL;DR: The Handbook of Research on Methods and Techniques for Studying Virtual Communities as mentioned in this paper satisfies the need for methodological consideration and tools for data collection, analysis and presentation in virtual communities, making this reference a comprehensive source of research for those in the social sciences and humanities.
Journal ArticleDOI
Choosing Wavelet Methods, Filters, and Lengths for Functional Brain Network Construction.
TL;DR: The results suggest that the choice of wavelet method and length significantly alters the reliability and sensitivity of these methods in estimating values of metrics drawn from graph theory, and demonstrate the importance of reporting the choices utilized in neuroimaging studies.
Journal ArticleDOI
History, Evolution and Future of Big Data and Analytics: A Bibliometric Analysis of Its Relationship to Performance in Organizations
Sasa Batistic,Paul van der Laken +1 more
TL;DR: In this paper, the authors present a review of big data and analytics (BDA) research, using three bibliometric methods: co-citation analysis, historical evolution of BDA and performance research and its substreams through algorithmic historiography.
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.