Neural attentive influence maximization model in social networks via reverse influence sampling on historical behavior sequences

Abstract

Influence maximization in social networks aims to identify users with the highest influence in the network and leverage them as initial spreaders to maximize the revenue of influence. Recently, an increasing number of studies have drawn attention to the study of influence maximization in multi-entity social networks, achieving some degrees of success. However, most of them still have two major shortcomings. First, most of the previous work oversimplifies the correlation between the target entity and other entities, and overlooks the influence of user’s interest preference on information propagation. Second, the majority of existing methods face a trade-off between operational efficiency and solution accuracy, making it difficult to be effectively applied to large social networks. To fill this gap, this paper proposes a Deep neural network model based on Historical Behavior Sequences, named DHBS. DHBS consists of two core components: a Deep neural network model Incorporating Multi-head Self-attention, named DIMS; and a Reverse Influence Sampling algorithm based on Historical Behavior Sequence, named RIS-HBS. Specifically, DIMS captures the dynamic interest changes of users through its attention mechanism, enabling more accurate modeling of entity correlation in multi-entity social networks. RIS-HBS efficiently identifies a set of high-influential users for the target entity by incorporating the concept of reverse influence sampling. Experimental results on four publicly available network datasets demonstrate that DHBS outperforms baseline methods in terms of prediction accuracy, expected spread, and scalability, and DIMS plays a key role on RIS-HBS in the DHBS model.