HCCKshell: A heterogeneous cross-comparison improved Kshell algorithm for Influence Maximization

Influence maximization (IM) has been extensively researched in the information propagation field and applied in various domains. However, existing studies on the IM have primarily focused on network structure, and lack the in-depth exploration of online network complexities, like personal history or preference. In this paper, a heterogeneous cross-comparison improved Kshell algorithm (HCCKshell) is proposed to solve IM, applying users’ multi-dimensional attributes in the propagation, including social history and topological structure. Specifically, the model learns users’ potential representation of historical content preferences and topological structure based on the Encoder and GCN thoughts, then defines the heterogeneous similarity and the heterogeneous information entropy to measure users’ influence ability and provide reliability assurance on the propagation. To improve the performance, a cross-comparison improved K-shell heuristic algorithm based on the heterogeneous information entropy is proposed to find a valid influential seed set. Furthermore, the experiments on multiple real large-scale datasets and their results indicate that our HCCKshell algorithm is more effective than baseline algorithms on both effect and performance.