A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction

Information diffusion prediction is an essential task in understanding the dissemination of information on social networks. Its objective is to predict the next user infected with a piece of information. While previous work focuses primarily on the analysis of diffusion sequences, recent work shifts towards examining social network connections between users. During the diffusion of information, users are expected to send and receive information. However, few works analyze the sending and receiving behavior of users during information diffusion. We design a Diffusion Simulation User Behavior Perception Attention Network (DSUBPAN). First, based on the social network graph, we construct a diffusion simulation heterogeneous network graph, which simulates diffusion, and obtain the sending and receiving behavior of users during information diffusion. Second, we utilize a user behavior fuse transformer to fuse different user behaviors. Then, we employ an attention network to perceive the time information and user sequence information in the information diffusion sequence. Finally, we utilize a dense layer and a softmax layer to predict the next infected user. Our model outperforms baseline methods on two real-world datasets, demonstrating its effectiveness.