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各种各样的复杂系统可以表示为网络。例如社交网络用节点表示人,用边表示人与人之间的关系;而生物网络通常用生物化学分子表示为节点,用边缘表示它们之间的反应。近年来的研究大多集中在了解网络的演化和组织,以及网络拓扑对系统动力学和行为的影响。在网络中寻找社区结构是理解它们所代表的复杂系统的另一步。
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Near linear time algorithm to detect community
structures in large-scale networks
Usha Nandini Raghavan,1 Réka Albert,2 and Soundar Kumara1 1
Department of Industrial Engineering, The Pennsylvania State University, University
Park, Pennsylvania 16802, USA 2
Department of Physics, The Pennsylvania State University, University Park,
Pennsylvania 16802, USA
( Received 9 April 2007; published 11 September 2007 )
PHYSICAL REVIEW E 76, 036106 (2007)
Introduction
A wide variety of complex systems can be represented as networks. For
example, social networks are represented by people as nodes and their
relationships by edges; and biological networks are usually represented by
biochemical molecules as nodes and the reactions between them by edges.
Most of the research in the recent past focused on understanding the evolution
and organization of such networks and the effect of network topologyon the
dynamics and behaviors of the system . Finding community structures in
networks is another step toward understanding the complex systems they
represent.
Introduction
A community in a network is a group of nodes that are similar to each
other and dissimilar from the rest of the network. It is usually thought of as a
group where nodes are densely interconnected and sparsely connected to
other parts of the network. There is no universally accepted definition for a
community, but it is well known that most real world networks display
community structures. The goal of a community detection algorithm is to find
groups of nodes of interest in a given network. For example, a community in
the WWW network indicates a similarity among nodes in the group. Hence if
we know the information provided by a small number of web pages, then it
can be extrapolated to other web pages in the same community. Communities
in social networks can provide insights about common characteristics or
beliefs among people that makes them different from other communities. In
biomolecular interaction networks, segregating nodes into functional modules
can help identify the roles or functions of individual molecules .
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