基于引力模型及相对路径数的节点重要度评估算法

准确度量复杂网络中节点的重要度对于研究网络结构和功能等方面具有重要的指导意义。现有多数节点重要度评估算法考虑了节点及其邻居节点的相关信息,却忽略了节点间的拓扑结构对节点重要度的影响。针对此问题,提出了基于引力模型及相对路径数的节点重要度评估算法。该算法首先分析了相对最短路径数对节点间信息传播的影响效果,同时考虑到非最短路径及路径距离等因素的影响,然后以三阶范围内邻居节点与中心节点的相互作用力之和定义节点重要度值,最后在六个真实网络中进行仿真实验。实验结果表明,所提算法不仅能有效区分网络中不同节点之间的重要度差异,还能准确度量网络节点的重要度大小。

Node importance evaluation algorithm based on gravity model and relative path number

Accurately measuring the importance of nodes in complex networks has important guiding significance for the study of network structure and functions. Most existing node importance evaluation algorithms consider the relevant information of the node and its neighbor nodes, but ignore the influence of the topological structure between nodes on the importance of the node. To solve this problem, this paper proposed a node importance evaluation algorithm based on gravity model and relative path number. The algorithm firstly analyzed the effect of the relative shortest path number on the information dissemination between nodes, and considered the influence of factors such as non-shortest path and path distance, then defined the node importance by the sum of the interaction force between the neighbor node and the center node in the third-order range, and finally simulated it in six real networks. Experimental results show that the proposed algorithm can not only effectively distinguish the importance of different nodes in the network, but also accurately measure the importance of network nodes.