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针对聚集系数未涉及间接邻居连通性和无法正确描述大节点度网络节点的问题,提出聚集度的新度量-邻居系数,并基于其统计意义提出邻居系数网络模型。邻居系数从邻居演化的角度描述聚集度,定义为网络节点的间接邻居也是其直接邻居的概率,分析表明邻居系数可有效地描述各种网络节点的聚集度。邻居系数模型是通过引入局域连接这一邻居演化机制对Barabási-Albert(BA)无尺度网络模型的扩展。仿真结果表明邻居系数网络模型既具有可调的聚集度,又保持节点度的幂率分布。
Aiming at the problem that the aggregation coefficient does not involve the connectivity of indirect neighbors and can not correctly describe the degree of nodes in large nodes, a new measure of the degree of neighbors (neighbors) is proposed, and the neighbor coefficient network model is proposed based on its statistical significance. The neighbor coefficient describes the degree of agglomeration from the point of view of the evolution of the neighbor. It is defined as the probability that the indirect neighbor of the network node is also its immediate neighbor. The analysis shows that the neighbor coefficient can effectively describe the degree of agglomeration of various network nodes. The neighbor coefficient model is an extension of Barabási-Albert (BA) scale-free network model by introducing the neighbor evolution mechanism of local connection. The simulation results show that the neighbor coefficient network model not only has adjustable degree of aggregation but also keeps the node power rate distribution.