A WEIGHTED LOCAL-WORLD EVOLVING NETWORK MODEL BASED ON THE EDGE WEIGHTS PREFERENTIAL SELECTION
A WEIGHTED LOCAL-WORLD EVOLVING NETWORK MODEL BASED ON THE EDGE WEIGHTS PREFERENTIAL SELECTION
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DOI：
10.1142/s0217979213500392
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发表时间：
2013-05-10
影响因子：
1.7
通讯作者：
Wang, Haitang
中科院分区：
文献类型：
--
作者：
Li, Ping;Zhao, Qingzhen;Wang, Haitang
在本文中，我们使用边权重优先连接机制来构建一个新的加权网络的局域世界进化模型。与以往的文献不同的是，该模型的局部世界由边组成，而不是由节点组成。每个时间步，我们通过边权重优先选择，将一个新节点连接到局部世界中的两个现有节点。理论分析和数值模拟表明，局域世界的尺度对权值分布、强度分布和度分布都有影响。给出了聚集度和传染病传播动力学的仿真结果。我们的网络模型的权值动态可以刻画现实网络的结构，如线虫的神经网络和在线社交网络。
In this paper, we use the edge weights preferential attachment mechanism to build a new local-world evolutionary model for weighted networks. It is different from previous papers that the local-world of our model consists of edges instead of nodes. Each time step, we connect a new node to two existing nodes in the local-world through the edge weights preferential selection. Theoretical analysis and numerical simulations show that the scale of the local-world affect on the weight distribution, the strength distribution and the degree distribution. We give the simulations about the clustering coefficient and the dynamics of infectious diseases spreading. The weight dynamics of our network model can portray the structure of realistic networks such as neural network of the nematode C. elegans and Online Social Network.