Identification Of The Most Influential Nodes Based On The Diffusion K-truss Decomposition And Its Application

With the rapid development of information technology,the human living environment is also more and more network.Identification and protection of crucial nodes will be of great significance,For example,identifying the crucial nodes in the network accurately and using the crucial nodes as information sources can effectively promote the rapid diffusion of information and suppress the spread of the virus.In the algorithm of identifying the most influential node centrality,K-truss decomposition can effectively identify the most influential nodes.Nevertheless,the K-truss decomposition simply resolves the network according to edge clustering,and is unable to resolve the network effectively because of the impact of core-like group.Thus,On the basis of the edge clustering characteristics,we also consider the diffusion characteristics of the edge,and make the corresponding work to identify the most influential nodes in the network and optimize the network structure to promote information diffusion and suppress virus spread.The specific summary is as follows:1.We propose an improved diffusion K-truss decomposition method by considering both the diffusion and clustering of edges to eliminate the impact of core-like group on identifying influential nodes.To validate the effectiveness of the proposed method,we compare it with other typical methods over six real complex networks,the proposed diffusion K-truss decomposition algorithm can effectively disintegrate the core-like group and accurately identify the influential nodes.2.In this paper,by considering the relationship between the clustering characteristics and diffusion characteristics of edges,we propose a network structure optimization algorithm for information propagation.It is then applied to optimize the structures of four real networks,and verified the effective range of information transmission before and after the optimization of network structure from the classical independent cascade model.The results show that the network topology optimized by the proposed algorithm can effectively improve the range of information transmission.Moreover,the number of leaf nodes in the optimized network is reduced,and the clustering coefficient and the average path length are also reduced.3.Considering the relationship between the cluster characteristics and the diffusion characteristics of the edges,a structure optimization algorithm is proposed to suppress virus propagation by.In the four real networks,the classical virus propagation SIR model is used to compare the effective spreading range and diffusion speed of virus propagation before and after optimization of network structure.Experimental results show that the network topology optimized by the proposed algorithm can effectively suppress the virus spreading and reduce the virus spread range and slow down the propagation speed,and the virus propagation threshold of the network is improved.Moreover,the maximum degree of the optimization network,the total clustering ability and diffusion capacity of the edge will be reduced.