A evolving network model generated by random walk and policy attachment
一种基于随机行走和策略连接的网络演化模型

Real-world networks always present some complex network properties simultaneously, such as small-world, scale-free, 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 itself 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.