Research On Influential Nodes Identification In Complex Networks

The development of complex networks in various fields has brought great convenience to the production and life of human beings.Complex networks promote the production and life of human beings,but also bring negative impacts,such as large area blackout,traffic paralysis,computer network attack and epidemic disease transmission.Therefore,to effectively predict and control the network,we need to conduct a more in-depth and detailed analysis and research on various complex network systems,identify and evaluate the important nodes that affect the network structure and function.In view of the complex network system vulnerability,we propose four centrality algorithms to evaluate the node importance using local information and global information contained in the complex network node.The main research contents and innovations are as follows:(1)Based on the local information of the complex network,we propose an influential nodes identification algorithm based on network diffusion mechanism.According to the empirical observations,an individual in real word usually influences its nearest neighbors and next nearest neighbors.The influence of the individual spreads from center to around and follows a gradient descent rule.We propose a new method to identify influential nodes in complex networks.This measure describes the influence spread rule of the real world well.Susceptible-Infected(SI)model is utilized to evaluate the performance of the proposed method.Numerical examples are provided to demonstrate the efficiency of the proposed method.(2)Based on the global information of complex networks,we propose an influential nodes identification algorithm based on global efficiency and random walk mechanism respectively1)We use network global efficiency by removing edges to propose a new centrality measure for identifying influential nodes in complex networks.Differing from the traditional network global efficiency,the proposed measure is determined by removing edges from networks,not removing nodes.Instead of static structure properties which are exhibited by other traditional centrality measures,we focus on the perspective of dynamical process and global information in complex networks.Susceptible-Infected(SI)model is utilized to evaluate the performance of the proposed method.Numerical examples are provided to demonstrate the efficiency of the proposed method.2)We propose a novel centrality method,called Absorption Centrality,to measure the influence of all the entities of complex networks.Based on random walks,we start with the perspective of dynamical process,instead of static structure properties which are exhibited by other traditional centrality measures.Susceptible-Infected(SI)model is utilized to evaluate the performance of the proposed method.Numerical examples are provided to demonstrate the efficiency of the proposed method.(3)Based on the local and global information of complex networks,we propose an influential nodes identification algorithm based on fusion centrality.We proposed a new method to compromise and aggregate these different results for identifying influential nodes in complex networks.Different centralities are taken into account to aggregate in our proposed method.The proposed method is determined by the Euclidean distance of three centrality measures.Susceptible-Infected(SI)model is utilized to evaluate the performance of the proposed method.Numerical examples show that the proposed method is an effective way to identify influential nodes.