基于多重影响力矩阵的有向加权网络节点重要性评估方法

发布时间：2018-05-12 14:41

本文选题：有向加权网络 + 节点重要性　；参考：《物理学报》2017年05期

【摘要】：本文基于有向加权网络模型,构建了三个影响力矩阵,并利用层次分析法对其赋权求和,形成多重影响力矩阵,从而提出了一种基于该矩阵的节点重要性评价方法.该方法通过新定义的交叉强度指标,来表征节点的局部重要性;利用金网节点对待评估节点的重要性影响总值,来表征节点在全网中的相对重要性.在分析影响节点对待评估节点的影响比例时,既考虑到节点间的距离因素,又引入了最短路径条数因素;既考虑了该影响节点对网络中其他节点的影响关系,又考虑了网络中其他节点对该待评估节点的影响关系,使得评价方法更加全面.将算法运用于ARPA网络,结果表明,该方法能有效地区分各节点之间的差异,最后,对实验结果进疔连锁故障的仿真对比实验,进一步验证了方法的有效性.
[Abstract]:In this paper, based on the directed weighted network model, three influence matrices are constructed, and the sum of their weights is obtained by using the analytic hierarchy process (AHP) to form a multi-influence matrix, and a nodal importance evaluation method based on the matrix is proposed. In this method, the local importance of nodes is represented by the newly defined cross intensity index, and the relative importance of nodes in the whole network is represented by the total value of the importance of the nodes in the evaluation network. In the analysis of the impact ratio of the nodes on the evaluation nodes, the factors of distance between nodes and the number of shortest path bars are taken into account, and the influence relationship of the affected nodes on other nodes in the network is considered. Considering the influence of other nodes in the network on the node to be evaluated, the evaluation method is more comprehensive. The algorithm is applied to ARPA network, and the results show that the method can effectively distinguish the differences between nodes. Finally, the simulation and contrast experiment of the experimental results for cascading faults of clove furuncle proves the effectiveness of the method.
【作者单位】：上海理工大学管理学院;
【基金】：国家自然科学基金(批准号:71571119)资助的课题~~
【分类号】：O157.5

【相似文献】