一种改进的基于信息传播率的复杂网络影响力评估算法
发布时间：2018-10-17 11:18
【摘要】：评价网络中节点的信息传播影响力对于理解网络结构与网络功能具有重要意义.目前,许多基于最短路径的指标,如接近中心性、介数中心性以及半局部(SP)指标等相继用于评价节点传播影响力.最短路径表示节点间信息传播途径始终选择最优方式,然而实际上网络间的信息传播过程更类似于随机游走,信息的传播途径可以是节点间的任一可达路径,在集聚系数高的网络中,节点的局部高聚簇性有利于信息的有效扩散,若只考虑信息按最优传播方式即最短路径传播,则会低估节点信息传播的能力,从而降低节点影响力的排序精度.综合考虑节点与三步内邻居间的有效可达路径以及信息传播率,提出了一种SP指标的改进算法,即ASP算法.在多个经典的实际网络和人工网络上利用SIR模型对传播过程进行仿真,结果表明ASP指标与度指标、核数指标、接近中心性指标、介数中心性指标以及SP指标相比,可以更精确地对节点传播影响力进行排序.
[Abstract]:It is very important to evaluate the influence of the nodes in the network to understand the network structure and network function. At present, many indicators based on the shortest path, such as near-centrality, intermediate-centrality and semi-local (SP), are used to evaluate the influence of node propagation. The shortest path means that the best way to spread information between nodes is always chosen. However, in fact, the process of information transmission between networks is more similar to random walk, and the path of information transmission can be any reachable path between nodes. In the network with high agglomeration coefficient, the local high clustering of nodes is conducive to the effective diffusion of information. If the information is propagated according to the optimal mode of transmission, that is, the shortest path, the ability of the node to spread information will be underestimated. In order to reduce the ranking accuracy of node influence. Considering the effective reachable path between nodes and three-step neighbors and the information propagation rate, an improved SP index algorithm, ASP algorithm, is proposed. The SIR model is used to simulate the propagation process in several classical networks and artificial networks. The results show that the ASP index is compared with the degree index, the kernel index, the close centrality index, the intermediate central index and the SP index. Node propagation influence can be sorted more accurately.
【作者单位】： 国防科技大学信息系统工程重点实验室;国防大学联合勤务学院;
【基金】：国家自然科学基金(批准号:61302144,61603408)资助的课题~~
【分类号】：O157.5
[Abstract]:It is very important to evaluate the influence of the nodes in the network to understand the network structure and network function. At present, many indicators based on the shortest path, such as near-centrality, intermediate-centrality and semi-local (SP), are used to evaluate the influence of node propagation. The shortest path means that the best way to spread information between nodes is always chosen. However, in fact, the process of information transmission between networks is more similar to random walk, and the path of information transmission can be any reachable path between nodes. In the network with high agglomeration coefficient, the local high clustering of nodes is conducive to the effective diffusion of information. If the information is propagated according to the optimal mode of transmission, that is, the shortest path, the ability of the node to spread information will be underestimated. In order to reduce the ranking accuracy of node influence. Considering the effective reachable path between nodes and three-step neighbors and the information propagation rate, an improved SP index algorithm, ASP algorithm, is proposed. The SIR model is used to simulate the propagation process in several classical networks and artificial networks. The results show that the ASP index is compared with the degree index, the kernel index, the close centrality index, the intermediate central index and the SP index. Node propagation influence can be sorted more accurately.
【作者单位】： 国防科技大学信息系统工程重点实验室;国防大学联合勤务学院;
【基金】：国家自然科学基金(批准号:61302144,61603408)资助的课题~~
【分类号】：O157.5
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