Research On Node Importance Evaluation Method Based On Complex Network Structure Features

The research on importance ranking of complex network nodes is an important branch in the field of complex networks.Finding key nodes in a particular network has a huge effect on the entire network.In the real world,the spread of rumors,the protection of power facilities networks,and the discovery of important people on social networks are inseparable from the issue of node importance.At present,the algorithms of complex networks rarely take into account the structural characteristics of the network.Therefore,this paper analyzes the structural characteristics of complex networks in depth,and improves the importance of complex network nodes from the macro and micro perspectives of the network.The effect of mining important nodes.First,in Micro aspect,the current traditional node importance methods do not take into account the damage to the network structure,and are not suitable for social networks and other issues.An improved social network based on Articulation Point Removal Rank is proposed.Method for evaluating important nodes of the network APRR.The Tarjan algorithm is used to dynamically find and remove the cut point of the largest connected component of the social network,and the order in which these nodes are removed is used as the ranking result of the key nodes in the social network.Four real social networks are used as experimental simulation data,and compared with existing algorithms,the robustness test is performed.The simulation results show that the important nodes obtained by using APRR have better results on the robustness evaluation standard,and can make the entire network faster.Therefore,the APRR algorithm can effectively obtain the important nodes in social networks.Second,in Macro aspect,the results of PageRank methods are too centralized and do not take into account the structural characteristics of communities in complex network.To solve this problem,an improved Nodes Importance Ranking method CDPR based on complex network Community Detection is proposed.According to the result of Community Detection of complex networks by Label Propagation Algorithm(LPA),the internal and external connection relationship of community is transformed into the probability representation of community selection;According to the probability of Community selection,a certain proportion of candidate key nodes are extracted from each of them respectively.Finally,these candidate nodes are reordered and the key node sorting results are obtained.Using four real complex networks as experimental data,to compared with some existing algorithms,the SIR performance experiments are carried out respectively.The Experiments show that CDPR has a better effect on overall propagation performance.CDPR algorithm can effectively sort the importance of nodes in complex networks.