加权网络中基于冗余边过滤的k-核分解排序算法

发布时间：2019-03-28 15:33

【摘要】：k-核分解排序法对于度量复杂网络上重要节点的传播影响力具有重要的理论意义和应用价值,但其排序粗粒化的缺陷也不容忽视.最新研究发现,一些真实网络中存在局域连接稠密的特殊构型是导致上述问题的根本原因之一.当前的解决方法是利用边两端节点的外部连边数度量边的扩散性,采取过滤网络边来减少这种稠密结构给k-核分解过程造成的干扰,但这种方法并没有考虑现实网络上存在权重的普遍性.本文利用节点权重和权重分布重新定义边的扩散性,提出适用于加权网络结构的基于冗余边过滤的k-核分解排序算法:filter-core.通过世界贸易网、线虫脑细胞网和科学家合著网等真实网络的SIR(susceptible-infectedrecovered)传播模型的仿真结果表明,该算法相比其他加权k-核分解法,能够更准确地度量加权网络上具有重要传播影响力的核心节点及核心层.
[Abstract]:The K-kernel decomposition sorting method has important theoretical significance and application value for measuring the propagation influence of important nodes in complex networks, but its defect of coarse sorting can not be ignored. Recent research has found that the existence of a special configuration with dense local connections in some real networks is one of the root causes of the above problems. The current solution is to reduce the interference caused by this dense structure to the k-kernel decomposition process by taking advantage of the diffusivity of the external edges of the nodes at both ends of the edge to measure the diffusivity of the edges and to filter the edge of the network to reduce the interference caused by this dense structure. However, this method does not take into account the universality of the weight on the real network. In this paper, we propose a k-kernel decomposition sorting algorithm based on redundant edge filtering for weighted network structure using node weight and weight distribution to redefine the diffusivity of edges. This paper proposes a k-kernel decomposition sorting algorithm based on redundant edge filtering: filter-core. The simulation results of the SIR (susceptible-infectedrecovered) propagation model of real networks, such as the World Trade Network, the nematode brain cell network and the co-authoring network of scientists, show that the algorithm is compared with other weighted k-kernel decomposition methods. It can more accurately measure the core nodes and core layers with important propagation influence on weighted networks.
【作者单位】：西南财经大学经济信息工程学院;河南理工大学计算机科学与技术学院;
【基金】：国家自然科学基金(批准号:61602331) 中央高校基本科研业务费专项资金(批准号:JBK170133,JBK160130,JBK150503) 四川省教育厅科研基金(批准号:J17ZB0434) 互联网金融创新及监管协同创新中心资助的课题~~
【分类号】：O157.5;O223

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