An Influence Maximization Algorithm Based on Improved K-Shell in Temporal Social Networks

• DOI: 10.32604/cmc.2023.036159
• Publication Date: 2023-03-31T03:32:22Z
• Authors: Wenlong Zhu; Yu Miao; Shuangshuang Yang; Zuozheng Lian; Lianhe Cui
• Keywords: Maximization; Degree (music)

Influence maximization of temporal social networks (IMT) is a problem that aims to find the most influential set nodes in network so their information can be widely spread. To solve IMT problem, we propose an influence algorithm based on improved K-shell method, namely (KT). The takes into account global and local structures networks. First, obtain kernel value <i>Ks</i> each node, scope, it layers according characteristic by improving method. Then, calculation method comprehensive degree proposed weigh nodes. Finally, node with highest core layer selected as seed. However, seed selection strategy KT easily lose some Thus, optimizing strategy, this paper proposes efficient heuristic called for (KTIM). According hierarchical distribution cores, adds near central candidate set. It then searches seeds degree. Experiments show KTIM close best performing graph (IMIT) terms effectiveness, but runs at least order magnitude faster than it. Therefore, considering effectiveness efficiency simultaneously networks, works better other baseline algorithms.