Analysis Of Influence Maximization In Complex Networks

In recent years,the research on the influence of complex networks has gradually become a research hotspot in many fields.Its main research content is divided into node importance ranking and influence evaluation problems.This paper mainly focuses on the fact that traditional algorithms cannot fully find influential nodes,some key nodes are neglected,resulting in inaccurate ranking of selected nodes;or only considering the attributes of the nodes in the network,not fully considering the division of the whole network structure,resulting in the final selection of the seed nodes influence range is not wide enough to explore.Firstly,for the problem that the algorithms only consider degrees or cores,and does not consider the nodes in the core of network structure,which leads to the final influence range is not wide enough,an influence maximization algorithm based on structural holes and cores is proposed.By calculating the number of cores,degrees and finding out the structural holes with higher scores,the importance of nodes is evaluated more comprehensively,so as to find the more influential nodes.Secondly,for the problem that the traditional influence maximization algorithms only consider the internal attributes of nodes and ignores the external attributes between nodes,which leads to the limited influence range of the selected nodes.CSCA divides the network into different modules by community partition,finds out the location of key nodes in each community,and then selects important nodes by greedy thinking,so as to fully consider the internal and external attributes of nodes,so as to make information spread faster and more widely in complex networks and improve network connectivity.Finally,the experimental analysis of KS2 H and CSCA algorithms is given.In the real datasets,the independent cascade propagation model is used to simulate the information propagation.The infection range and time consumption indicators are used to analyze the influence span and time consumption of KS2 H,CSCA and the comparison algorithms to select the seed nodes,and verify the effectiveness of the two proposed algorithms.And use the network efficiency and network blockbuster indicators to analyze the impact of the nodes selected by the algorithms on the network,and verify the impact of the nodes selected by CSCA on the network connectivity.