Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks

Due to the growing interconnections of social networks, the problem of influence maximization has been extended from a single social network to multiple social networks. However, a critical challenge of influence maximization in multi-social networks is that some initial seed nodes may be unable to be active, which obviously leads to a low performance of influence spreading. Therefore, finding substitute nodes for mitigating the influence loss of uncooperative nodes is extremely helpful in influence maximization. In this paper, we propose three substitute mining algorithms for influence maximization in multi-social networks, namely for the Greedy-based substitute mining algorithm, pre-selected-based substitute mining algorithm, and similar-users-based substitute mining algorithm. The simulation results demonstrate that the existence of the uncooperative seed nodes leads to the range reduction of information influence. Furthermore, the viability and performance of the proposed algorithms are presented, which show that three substitute node mining algorithms can find suitable substitute nodes for multi-social networks influence maximization, thus achieves better influence.