一种信息传播促进网络增长的网络演化模型

为了研究信息传播过程对复杂网络结构演化的影响,提出了一种信息传播促进网络增长的网络演化模型,模型包括信息传播促进网内增边、新节点通过局域世界建立第一条边和信息传播促进新节点连边三个阶段,通过多次自回避随机游走模拟信息传播过程,节点根据路径节点的节点度和距离与其选择性建立连接。理论分析和仿真实验表明,模型不仅具有小世界和无标度特性,而且不同参数下具有漂移幂律分布、广延指数分布等分布特性,呈现小变量饱和、指数截断等非幂律现象,同时,模型可在不改变度分布的情况下调节集聚系数,并能够产生从同配到异配具有不同匹配模式的网络.

A complex network evolution model for network growth promoted by information transmission

In many real complex networks, information transmission occurs all the time. To study the effects of information transmission on the complex network evolution, we propose a new model for network growth promoted by the information transmission. The model includes three major steps: (i) New links attached to the nodes on the information transmission path, whose source point is chosen preferentially; (ii) the first link of the new node attached to the nodes in the local-world; (iii) other links of the new node attached to the nodes on the information transmission path, whose source point is the new node. The process of information transmission is simulated by self-avoiding random walk, and by considering the local information including its degree and distance; selective connection is established between the nodes on the information transmission path. Theoretical analysis and numerical simulation results show that the proposed model can not only reproduce small-world and scale-free network characteristics, but also indicate that “shift power-law distribution” and “truncated power law” function may form for different parameters which have some non-power-law features, such as exponential cutoff, and saturation for small variables. Moreover, in our model, the clustering coefficient is tunable without changing the degree distribution, and the model can also construct a network with assortative or disassortative mixed pattern.
Keywords： complex network information transmission evolving model self-avoid random walk