Real-world networks always present some complex network properties .simultaneously, such as small-world, scalefree, high clustering and assortative/disassortative mixing, etc. , but only part of these properties can be reproduced in most of complex network models. In this paper, a new complex network model generated by random walk and policy attachment(RAPA) is proposed. A new peer constructs a local world by random walking, and attaches itseff to peers in the local world following the policy of "random selection", "poverty alleviation" or "favoring the rich". The results of analysis computing and simulation demonstrate that RAPA model can reproduce not only small-world and scale-free features, but some non-power-law features such as exponential cutoff and saturation for small variables. In addition to these, RAPA model also constructs some networks with evident clustering structure and assortative/disassortative mixing pattern.%本文提出了一个基于随机行走和策略选择的复杂网络局域演化模型RAPA.新节点加入系统不需要全局知识,而是通过随机行走构造局域世界;然后依据概率采用随机连接,"扶贫"连接或"亲富"连接策略,从局域世界中选择节点增加连接边;最终自组织演化具有幂律特点的复杂网络.初步的解析计算和仿真实验都表明,RAPA模型不仅重现了具有小世界特性、整体上的无标度特性,还可以演化出小变量饱和以及指数截断等现象,同时也具有明显的聚类特性,并能够构造出同配或异配等不同混合模式的网络.
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