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JournalISSN: 1539-3755

Physical Review E

About: Physical Review E is an academic journal. The journal publishes majorly in the area(s): Liquid crystal & Phase transition. It has an ISSN identifier of 1539-3755. Over the lifetime, 62628 publication(s) have been published receiving 1883576 citation(s). The journal is also known as: Phys. Rev. E & Physical Review E: Statistical, Nonlinear, Biological, and Soft Matter Physics. more


Open accessJournal ArticleDOI: 10.1103/PHYSREVE.69.026113
26 Feb 2004-Physical Review E
Abstract: We propose and study a set of algorithms for discovering community structure in networks-natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible "betweenness" measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the community structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex structure of networked systems. more

Topics: Clique percolation method (58%), Girvan–Newman algorithm (55%), Stochastic block model (55%) more

11,600 Citations

Open accessJournal ArticleDOI: 10.1103/PHYSREVE.70.066111
06 Dec 2004-Physical Review E
Abstract: The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers. more

5,826 Citations

Open accessJournal ArticleDOI: 10.1103/PHYSREVE.51.4282
Dirk Helbing1, Péter Molnár1Institutions (1)
01 May 1995-Physical Review E
Abstract: It is suggested that the motion of pedestrians can be described as if they would be subject to ``social forces.'' These ``forces'' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can also be applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: first, a term describing the acceleration towards the desired velocity of motion; second, terms reflecting that a pedestrian keeps a certain distance from other pedestrians and borders; and third, a term modeling attractive effects. The resulting equations of motion of nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically. more

Topics: Social force model (66%), Equations of motion (50%)

4,832 Citations

Open accessJournal ArticleDOI: 10.1103/PHYSREVE.69.066133
Mark Newman1Institutions (1)
18 Jun 2004-Physical Review E
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. more

4,673 Citations

Open accessJournal ArticleDOI: 10.1103/PHYSREVE.74.036104
Mark Newman1Institutions (1)
11 Sep 2006-Physical Review E
Abstract: We consider the problem of detecting communities or modules in networks, groups of vertices with a higher-than-average density of edges connecting them. Previous work indicates that a robust approach to this problem is the maximization of the benefit function known as ``modularity'' over possible divisions of a network. Here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix, which plays a role in community detection similar to that played by the graph Laplacian in graph partitioning calculations. This result leads us to a number of possible algorithms for detecting community structure, as well as several other results, including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong. The algorithms and measures proposed are illustrated with applications to a variety of real-world complex networks. more

Topics: Modularity (networks) (61%), Adjacency matrix (60%), Graph partition (60%) more

4,062 Citations

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Journal's top 5 most impactful authors

Boris A. Malomed

180 papers, 5.6K citations

Ying-Cheng Lai

159 papers, 7.7K citations

H. Eugene Stanley

151 papers, 13.5K citations

Itamar Procaccia

111 papers, 2.2K citations

Hans J. Herrmann

97 papers, 3.5K citations

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