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Degree dependence entropy descriptor for complex networks
  • 期刊名称:Advances in manufacturing
  • 时间:2013.7.7
  • 页码:284-287
  • 分类:TP393.08[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术] O241.8[理学—计算数学;理学—数学]
  • 作者机构:[1]Department of Information Operation and Command Training,NDU of PLA, [2]No.65047 Troops of PLA
  • 相关基金:supported by the National Natural Science Foundation of China(Grants Nos.61174156,61273189,71073172,61174035,61203140)
  • 相关项目:突发公共事件信息在互联网上的传播机理仿真研究
作者: 许相莉|
中文摘要:

In order to supply better accordance for modeling and simulation of complex networks,a new degree dependence entropy(DDE) descriptor is proposed to describe the degree dependence relationship and corresponding characteristic in this paper.First of all,degrees of vertices and the shortest path lengths between all pairs of vertices are computed.Then the degree dependence matrices under different shortest path lengths are constructed.At last the DDEs are extracted from the degree dependence matrices.Simulation results show that the DDE descriptor can reflect the complexity of degree dependence relationship in complex networks;high DDE indicates complex degree dependence relationship;low DDE indicates the opposite one.The DDE can be seen as a quantitative statistical characteristic,which is meaningful for networked modeling and simulation.

英文摘要:

In order to supply better accordance for mod eling and simulation of complex networks, a new degree dependence entropy (DDE) descriptor is proposed to describe the degree dependence relationship and corre sponding characteristic in this paper. First of all, degrees of vertices and the shortest path lengths between all pairs of ,ertices are computed. Then the degree dependence matrices under different shortest path lengths are con structed. At last the DDEs are extracted from the degree dependence matrices. Simulation results show that the DDE descriptor can reflect the complexity of degree dependence relationship in complex networks; high DDE indicates complex degree dependence relationship; low DDE indicates the opposite one. The DDE can be seen as a quantitative statistical characteristic, which is meaningful for networked modeling and simulation.

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