Research On Social Network Propagation Algorithm Based On Connected Positive Influence Dominating Set

By means of influencing the positive influence dominance set(PIDS)of the network,we can effectively guide public opinion and cultivate social atmosphere in social networks.PIDS has great practical significance for social issues such as smoking and drinking.For a subset of nodes in the network,if all nodes in the network have more than half of the neighbor nodes in the subset,then such a subset of nodes construct a PIDS of the network.The Connected Positive Influence Dominating Set(CPIDS)further requires the internal connectivity of the dominating set based on the positive influence of the dominating set.In the existing researches on PIDS,many algorithms are computationally intensive,not suitable for large social networks.In this paper,contrapose the shortcomings of the existing methods,we proposes an efficient heuristic algorithm to construct the CPIDS ins social networks.The experimental results show that the proposed method outperforms the existing large-scale network algorithms.Meanwhile,considering that the PIDS occupies relatively large proportion in the network and difficult to apply to the actual problems,this paper proposes the idea of applying the influence maximization problem on CPIDS in the network,and designs a two-step influence propagation algorithm LIBH based on CPIDS in the network,which maximizes the influence on the obtained CPIDS.After obtaining the CPIDS in the network,we selects some nodes as the seed node set,so that it can spread to the entire CPIDS after the two-step influence propagation,thereby further reducing the number of initial activation points.In this paper,the simulation experiments are carried out in synthetic networks and real-world network dataset respectively.The experimental results show that the performance of the proposed TBCM algorithm is better than the other three existing methods in both running time and the size of the solution set aspects.At the same time,for the proposed LIBH algorithm,the article compares it with the existing three algorithms that have representative effects of maximizing influence.And evaluate the three algorithms on the aspects of the solution of the seed node set size and runtime under the premise of affecting the whole CPIDS.The experimental results show that the proposed TBCM algorithm and LIBH algorithm have great advantages in terms of running time.