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Weighted Scaling in Non-growth Random Networks

Weighted Scaling in Non-growth Random Networks
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摘要 We propose a weighted model to explain the self-organizing formation of scale-free phenomenon in nongrowth random networks. In this model, we use multiple-edges to represent the connections between vertices and define the weight of a multiple-edge as the total weights of all single-edges within it and the strength of a vertex as the sum of weights for those multiple-edges attached to it. The network evolves according to a vertex strength preferential selection mechanism. During the evolution process, the network always holds its totM number of vertices and its total number of single-edges constantly. We show analytically and numerically that a network will form steady scale-free distributions with our model. The results show that a weighted non-growth random network can evolve into scMe-free state. It is interesting that the network also obtains the character of an exponential edge weight distribution. Namely, coexistence of scale-free distribution and exponential distribution emerges.
出处 《Communications in Theoretical Physics》 SCIE CAS CSCD 2012年第9期456-462,共7页 理论物理通讯(英文版)
基金 Supported by the National Natural Science Foundation of China under Grant No.60874080 the Commonweal Application Technique Research Project of Zhejiang Province under Grant No.2012C2316 the Open Project of State Key Lab of Industrial Control Technology of Zhejiang University under Grant No.ICT1107
关键词 weighted network random network non-growth scale-free distribution 随机网络 加权模型 权重分布 缩放 边缘连接 无标度网络 组织形成 选择机制
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