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  community structure
Soil animals are abundant in forest litter layer, but little attention has been paid to the vertical distribution of community structure of soil animals in the layers at different plant community succession stages.
      
Its results are more closer to the reality, especially for community structure.
      
The spatial pattern analysis of population and community is important to understand community structure and has become one key topic in modern plant ecology.
      
Monthly change of community structure of zoobenthos in Xiangxi Bay after impoundment of the Three Gorges Teservoir, China
      
We investigated the community structure of zoobenthos in Xiangxi Bay from August 2003 to August 2004.
      
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>=There have been numerous and various complex networks with the development of science, technology and human society, such as the Internet, the World Wide Web, the network of air lines, large-scale electric power networks, the structure of a piece of Very Large-Scale Integration (VLSI), the human social relationships, the neural networks, and the spreading path net of an infectious disease, etc. Empirical studies and theoretical modeling of networks have been the subject of a large body of recent research in...

>=There have been numerous and various complex networks with the development of science, technology and human society, such as the Internet, the World Wide Web, the network of air lines, large-scale electric power networks, the structure of a piece of Very Large-Scale Integration (VLSI), the human social relationships, the neural networks, and the spreading path net of an infectious disease, etc. Empirical studies and theoretical modeling of networks have been the subject of a large body of recent research in statistical physics and applied mathematics. A property that seems to be common to many networks is community structure, the division of network nodes into groups within which the network connections are dense, but between which they are sparser. Community detection in large networks is potentially very useful. Nodes belonging to a tight-knit community are more than likely to have other properties in common. For instance, in the world-wide-web, community analysis has uncovered thematic clusters. In biochemical or neural networks, communities may be functional groups, and separating the network into such groups could simplify functional analysis considerably. The problem of community detection is quite challenging and has been the subject of discussion in various disciplines, particularly in computer science and sociology. It is an NP complete problem and the fact that we often have no idea how many communities we wish to discover makes the process all the more costly. Many attempts to tackle the problem have been proposed, such as the Kernighan-Lin algorithm, spectral algorithm, G-N algorithm, Radicchi algorithm and so forth. However, these methods are more or less inaccurate, computationally demanding or specific to particular applications. In this paper, therefore, we propose another approach for modularity identification based on the evolutionary computation (EC). We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data.

现实世界中复杂网络无处不在,从因特网到万维网,从航空路线图到大型电力网格, 从超大规模集成电路图到人际关系网,从细胞神经网络到传染病传播过程等等。随着近年来对复杂网络性质的物理意义和数学特性的深入研究,人们发现许多实际网络都具有一个共同的性质, 即社区结构。整个网络是由若干个“社区”构成的,每个社区内部的节点之间的连接相对非常紧密,但是各个社区之间的连接却比较稀疏。

 
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