Social Network Influence Maximization Algorithm based on Dynamic Collaborative Optimization

In the influence maximization problem, existing methods based on greed and the centrality of network structure are difficult to strike a balance between accuracy and time complexity. Aiming at the limitations of existing algorithms, a dynamic collaborative optimization based social network influence maximization algorithm CPWA is proposed. This method estimates the influence of each node through the independent cascade model, adaptively adjusts the candidate pool and weight mechanism during iteration, and learns the historical search experience to find the optimal solution. Through the independent cascade model propagation experiments on four different scale networks, the superiority and accuracy of the proposed algorithm are proved by comparing it with the advanced benchmark method.