Wu Zhi-Xi, Xu Xin-Jian, Wang Ying-Hai
Institute of Theoretical Physics, Lanzhou University, Lanzhou Gansu 730000, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Jun;71(6 Pt 2):066124. doi: 10.1103/PhysRevE.71.066124. Epub 2005 Jun 23.
Motivated by the degree-dependent deactivation model generating networks with high clustering coefficient [K. Klemm, Phys. Rev. E. 65, 036123 (2002)], a weight-dependent version is studied to model evolving networks. The growth dynamics of the network is based on a naive weight-driven deactivation mechanism which couples the establishment of new active vertices and the weights' dynamical evolution. Both analytical solutions and numerical simulations show that the generated networks possess a high clustering coefficient larger than that for regular lattices of the same average connectivity. Weighted, structured scale-free networks are obtained as the deactivated vertex is target selected at each time step, and weighted, structured exponential networks are realized for the random-selected case.
受生成具有高聚类系数网络的度依赖失活模型（K. 克莱姆，《物理评论E》65，036123 (2002)）的启发，研究了一个权重依赖版本来对演化网络进行建模。网络的增长动态基于一种简单的权重驱动失活机制，该机制将新活跃顶点的建立与权重的动态演化耦合起来。解析解和数值模拟均表明，生成的网络具有比相同平均连通性的规则晶格更高的聚类系数。当在每个时间步以目标选择方式选择失活顶点时，可得到加权、结构化的无标度网络；而对于随机选择的情况，则可实现加权、结构化的指数网络。
