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Open AccessJournal ArticleDOI

Fast algorithm for detecting community structure in networks.

Mark Newman
- 18 Jun 2004 - 
- Vol. 69, Iss: 6, pp 066133-066133
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.

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Citations
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Proceedings ArticleDOI

Overlapping Community Detection by Collective Friendship Group Inference

TL;DR: This work focuses on the ability to find overlapping communities by aggregating the community perspectives of friendship groups, derived from egonets, and demonstrates that the algorithm not only finds overlapping communities, but additionally helps identify key members, which bind communities together.
Journal ArticleDOI

The community structure of human cellular signaling network.

TL;DR: Signal transduction data is extracted from KEGG to construct a cellular signaling network of Homo sapiens, which has 931 nodes and 6798 links in total and is found to be a scale-free network following a power-law of P(K)∼K −γ , with γ approximately equal to 2.2.
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Detecting research fronts using different types of weighted citation networks

TL;DR: This paper evaluates the performance of each type of weighted citation networks in detecting a research front by using the following measures of papers in the cluster: visibility, measured by normalized cluster size, speed, topological relevance, and density.
Journal ArticleDOI

A novel modularity-based discrete state transition algorithm for community detection in networks

TL;DR: A novel modularity-based discrete state transition algorithm (MDSTA) is proposed to obtain more optimal and stable solutions for community detection in networks and based on the heuristic information of the network, vertex substitute transformationoperator and community substitute transformation operator are proposed for global search.
Journal ArticleDOI

Exploration of distributed shared memory architectures for NoC-based multiprocessors

TL;DR: This paper focuses on the energy/delay exploration of a distributed shared memory architecture, suitable for low-power on-chip multiprocessors based on NoC, and the exploitation of the HwMMU primitives for the migration, replication, and compaction of shared data.
References
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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

宁北芳, +1 more
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

Mark Newman
- 01 Jan 2003 - 
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

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.
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