Influence Maximization in Temporal Social Networks with the Mixed K-Shell Method

Abstract

The study of influence maximizing in temporal social networks (IMT) is an important aspect of influence maximization (IM) research. Currently, two main types of algorithms can solve the IMT problem: greedy-based algorithms and heuristic-based algorithms. However, the greedy-based algorithm is too time-consuming to be used in practice, and most existing heuristic methods do not consider the attributes of nodes, resulting in these methods being unable to solve the IMT problem. Therefore, this paper proposes a mixed k-shell (MKS) algorithm, which considers nodes’ local and global attributes to characterize their influence and select seed nodes. At the local level, we consider the degree centrality of nodes, and at the global level, we propose the temporal k-shell decomposition (TKS) algorithm. Ultimately, the influence of a node is determined by combining the influence of itself and its neighbors. Experiments on four real temporal social networks show that MKS performs better in effectiveness than other heuristic baselines and can maintain a balance between effectiveness and efficiency, providing a useful solution for solving the IMT problem.