Research On Social Network Influence Maximization Algorithm Based On Time Sequential Relationship

How to make information spread quickly in the network is a hot research field in social networks.At present,research on this issue usually takes static social networks as the research object,and networks in real life cannot be simply abstracted as static social networks.For example,social networks and road networks only have connections between nodes at certain specific times.That is,the connection between nodes is time sequential.Therefore,in order to accurately obtain the influence range of the time sequential network,this paper studies the problem of maximizing the influence of the time sequential social network,that is,finding k nodes on the time sequential social network to maximize the spread of information.First,the weighted cascade model(WCM)based on static graphs cannot be applied to the problem of time sequential social networks.The WCM model is improved,and an improved weighted cascade model(IWCM)is proposed to make information can be disseminated on time sequential social networks through the IWCM dissemination model.Secondly,for the time sequential social network where information dissemination is limited to the first-level neighbor nodes,a two-stage time sequential social network influence maximization algorithm is designed.In the timing heuristic stage,the algorithm considers the effect of node degree and network timing characteristics on node influence,and selects the candidate node with the largest influence estimated value on this basis;In the timing greedy phase,the traditional greedy algorithm is optimized based on the idea of traditional greedy algorithm,and the most influential seed node is selected.Finally,the efficiency and accuracy of the Two-stage impact maximization algorithm(TIM)are verified through experiments.TIM algorithm combines the advantages of heuristic algorithm and greedy algorithm,reduces the calculation range of marginal revenue from all nodes in the network to candidate nodes,and greatly reduces the running time of the program under the premise of ensuring accuracy.Finally,for the time sequential social network where information is widely used in neighbor nodes within the second level,a timing-impact maximization algorithm based on the influence of neighbor nodes is proposed.This algorithm improves the traditional degree estimation algorithm.On the basis of considering the degree of the node,it adds the consideration of neighbor nodes,and proposes a second-level neighbor node influence measurement method.And on the basis of the measurement method,the overlap of the influence range between nodes is subtracted,and the time sequence is performed at the same time to form the Algorithm for maximizing influence of time series social network based on second-level neighbors(STIM).Finally,it is verified through experiments that the STIM algorithm has stronger pertinence and practicability than ordinary algorithms,and shows its superiority in the scope of influence and running time,and can better solve the problem of maximizing time sequential influence based on the influence of neighbor nodes.