| In our everyday life,complex networks have many uses.For example,they can be used to model and analyze many complex systems,we can see many complex networks such as epidemic transmission networks,aviation networks,citation networks,computer networks,and social networks.The emergence of complex networks has changed our understanding of complex systems,and its gradual formation is of great help to our study of complex systems that are usually difficult to control.The research on complex network in all aspects is also in full swing,especially with the rise of various social networks and the development of smart phones,the research on online social networks in all aspects has also entered the era of rapid development.The current academia has encountered a series of problems in the process of studying complex networks.One of the most important problems is the method that we mine the top key nodes in the analysis of complex networks.The existing literature offers several metrics,also called centrality measures which estimates importance using the structural properties of node,degree,closeness,betweenness,eigenvector centrality,Pagerank etc.In today’s society,the spread of information is more rapid with the development of the network,and the selection of key nodes in the complex network will greatly affect the cost and efficiency of information transmission.The selection of key nodes plays a crucial role in the rapid and efficient dissemination of information to all parts of the network.Through a lot of reading of literature and research data,we learned a lot of knowledge on complex networks theory and studied the advantage and disadvantage of key point player in studying key points in complex networks.The core idea of key point player algorithm is finding Maximizes the set of nodes that connect to other nodes.The importance of nodes is judged by the weight of node sets in the network.Key point player method based on greedy algorithm,the efficiency and algorithm complexity has a considerable disadvantage.In this article,we will focus on exploring the Influence maximization problem of complex networks.First we will extend from the most basic centrality to the key player sets,and then discusses the shortcomings of key point player sets and what can be improved.Finally we import the concept of clustering and propose a new method of clustering key player points and study the advantage of it on the basis of the linear threshold model,so that we can take care both performance and maximum diffusion rate. |
