Wenlong Zhu1,2,*, Yu Miao1, Shuangshuang Yang3, Zuozheng Lian1,2, Lianhe Cui1
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3095-3117, 2023, DOI:10.32604/cmc.2023.045646
Abstract Influence Maximization (IM) aims to select a seed set of size k in a social network so that information can be spread most widely under a specific information propagation model through this set of nodes. However, most existing studies on the IM problem focus on static social network features, while neglecting the features of temporal social networks. To bridge this gap, we focus on node features reflected by their historical interaction behavior in temporal social networks, i.e., interaction attributes and self-similarity, and incorporate them into the influence maximization algorithm and information propagation model. Firstly, we propose a node feature-aware voting… More >
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