Reserch On Social Network Influence Maximization Based On Community

In recent years,with the popularity of WeChat,Weibo,etc.,in life,social networks have gradually become indispensable in people's lives.Using online social networks,people can establish social relationships.Two people in the real world that are far apart can communicate and share ideas,and review the same hot events.Social networks have gradually become a valuable marketing medium.At the same time,people gradually found that advertising on social networks can get good feedback,and the problem of maximizing influence arise too.The traditional problem of influence maximizaton is mainly from the individual level to analyze influence,and it was seldomly considered that users in an online social network generally form an objective fact such as a community.It is an NP-hard problem to mine the most influential node in the network from the individual level.The greedy algorithm in the existing research can guarantee the approximate optimality of its solution,but its disadvantage is that the algorithm is on a large-scale network.Runtime costs are higher.Based on this,in order to improve the efficiency of the algorithm to solve this problem in large-scale networks,this paper proposes an algorithm NVPA-IM(Neighborhood Vectorhood Propagation Algorithm-Influence Maximization)based on network coarsening to maximize the influence.This algorithm uses the community structure of the network to select the K nodes that have the greatest influence.This article mainly includes the following points:First,in social networks,user relationships with the same attributes tend to be closer so that community structures formed.Mining community structure in the network plays an important role in understanding the spread of information in the network.Therefore,the first step in solving the problem of maximizing influence is to obtain the community structure of the network.For community division,this paper selects NVPA algorithms.At the same time,this paper selects some representative algorithms such as greedy algorithm FN(Fast Newman)and similarity aggregation algorithm H-Clustering and the label propagation algorithm LPA(Label Propagation Algorithm)for comparision.At last,this paper analysis the results of the division comparative from the perspective of influence.Second,this paper analyzes the seeds node selection algorithm from the perspective of the community.There are two main strategies for selecting nodes from the network: heuristic strategy and greedy strategy.The higher efficiency of the algorithm is the heuristic strategy.The degree center algorithm and the random algorithm are two typical heuristic strategies.In general,they are comparison algorithms.Greedy strategies are mainly greedy hill climbing algorithms.The accuracy of this algorithm is high,but its efficiency is low.Based on the nature of NVPA community partitioning algorithm,this paper proposes a new NVPA-IM seeds node selection algorithm based on degree center algorithm and analysis the NVPA-IM algorithm from the perspective of influence.