基于节点亲密度的重要性评估算法

识别复杂网络中的重要节点一直是社会网络分析和挖掘领域的热点问题,有助于理解有影响力的传播者在信息扩散和传染病传播中的作用。现有的节点重要性算法充分考虑了邻居信息,但忽略了邻居节点与节点之间的结构信息。针对此问题,考虑到不同结构下邻居节点对节点的影响力不同,提出了一种综合考虑节点的邻居数量和节点与邻居间亲密程度的节点重要性评估算法,其同时体现了节点的度属性和“亲密”属性。该算法利用相似性指标来测量节点间的亲密程度,以肯德尔相关系数为节点排序的准确度评价指标。在多个经典的实际网络上利用SIR(易感-感染-免疫)模型对传播过程进行仿真,结果表明,与度指标、接近中心性指标、介数中心性指标与K-shell指标相比,KI指标可以更精确地对节点传播影响力进行排序。

Importance Evaluation Algorithm Based on Node Intimate Degree

Identifying important nodes in complex networks has been a hot topic in the field of social network analysis and mi-ning,which helps to understand the role of influential communicators in information diffusion and the spread of infectious diseases.The existing algorithm of node importance takes neighbor information into account,but ignoring the structure information between node and neighbor node.To solve this problem,considering the different influence of the neighbor node to node under different structures,this paper proposes a node-importance evaluation algorithm that takes into account the number of neighbors of a node and the intimacy between nodes and neighbors,which embodies the degree of node and“intimate”attribute.In this algorithm,similarity index is used to measure the intimacy between nodes,and Kendall correlation coefficient is used to evaluate the accuracy of node ranking.The SIR(susceptible-infected-recovered)model is used to simulate the propagation process on several classical networks.The results show that compared with degree index,closeness centrality index,betweenness centrality index and K-shell index,KI index can rank the propagation influence of nodes more accurately.