A Maximizing Influence of Multiple Nodes Propagation Algorithm Based on Optimal Neighbor Discovery

A Maximizing Influence of Multiple Nodes Propagation Algorithm Based on Optimal Neighbor Discovery

Individual influence is one of the important factors of the evolution of information dissemination in social networks. Usually the ranking of node influence reflects the ability of single node to diffuse information. And when a set of multiple nodes distributed at each location of the network is propagated as a propagation source of the information, the sum of the influence of the plurality of nodes is no longer the addition of the influence of each node. We propose a new influence maximization algorithm named Max Neighbor Heuristic (MNH) combining Greedy Algorithm with Heuristic Algorithm. In order to balance the accuracy and efficiency in MNH, we considered the strategy of optimal neighbor discovery. By comparing the efficiency and accuracy of the algorithm with the three classical algorithms, we find that MNH algorithm has obvious advantages. Although the performance of MNH algorithm has a little bit of volatility, it has certain advantages in the average time-consuming and precision of the algorithm, which provides a new method and idea to solve the problem of maximizing influence of multi-node communication in social network.