The Maximum Impact Analysis Of Complex Network Based On Community

A complex network is a network that has some or all of the characteristics of a small world,no scale,and so on.Complex networks are spread across all areas of people's lives,such as power networks,transportation networks,economic networks,social networks,etc.Therefore,the study of complex networks has important theoretical and practical significance for today's real life.The order of importance and the maximization of influence of complex network nodes are the current research hotspots.The following two issues will be discussed.Firstly,for the neighborhood similarity-based sorting algorithm without considering the node community attribute,a ranking algorithm based on community attribute and neighborhood similarity node importance is proposed.The algorithm combines the attributes of the node itself,including the degree of the node and the degree of dependence of its neighbor nodes,and takes into account the importance of the community in which the node is located.It comprehensively evaluates the importance of the node and improves the effectiveness and accuracy of the algorithm.Secondly,in order to make information spread faster and more widely in the network,an influence maximization algorithm based on structural hole and community structure is proposed,which solves the problem that greedy algorithm and heuristic algorithm can not guarantee accuracy and time efficiency at the same time.problem.Aiming at the shortcomings of the existing algorithms,considering the structural hole characteristics of the nodes and the community structure of the network,the structural hole nodes with the pivot function and the community boundary nodes responsible for connecting the communities are used as the candidate seed node sets,in order to obtain accurate and effective seeds.The set enables information to be propagated to most nodes in the network in a short period of time.Finally,for the proposed ranking algorithm based on community attributes and neighborhood similarity node importance and the algorithm based on structural holes and community structure influence maximization,simulation experiments are carried out on several real network datasets according to different evaluation criteria.Experimental results show that the two algorithms can get better results.