一种高聚类无标度网络中的混合路由策略

Mixed Routing Strategy in Scale-free Networks with High Clustering

以无标度网络为基础,综合考虑新增社团结构和网络内部演化加边的机制,在度分布保持幂律分布的前提下,提高网络聚类系数。相比传统网络模型只能具有现实网络无标度或高聚类的特征,改进模型同时具有高聚类无标度特性,更贴近现实社会网络。以改进的网络模型为平台,提出了一种基于混合信息的路由策略。该策略综合运用网络动态和静态信息,将节点的介数作为其传递信息的能力,采用最短路径算法与优化的传递概率模型相结合,通过对可调参数的调控,使网络通信能力达到了最优。

Based on the scale-free network, and by comprehensively considering the structure of the newly-added community structure and the internal evolution of the network, the network clustering coefficient is improved on the premise that the degree distribution maintains the power-law distribution. Compared with the traditional network model, which only has the scale-free or high clustering features of the real network, the improved model has the high-clustering and scale-free characteristics, and this is fairly close to the real social network. With the improved network model as a platform, a routing strategy based on mixed information is proposed. This strategy, by comprehensively using dynamic and static information of the network, and with the number of nodes as its ability to deliver information, it combines the shortest path algorithm with an optimized transfer probability model. By adjusting the controllable parameters, optimization of network communication capability is realized.