SIGRL: Sociologically-Informed Graph Representation Learning for Social Influence Prediction

Social influence shapes various aspects of our lives, both in our daily physical interactions and in the virtual realm of the Web. Predicting social influence for individual users with high accuracy is essential. Traditional opinion dynamics models often depend on computer simulations, which require calibration using real-world data, resulting in significant manual effort and computational complexity. While recent deep-learning methods on graphs have demonstrated flexibility in modeling social interactions, they tend to overlook established scientific knowledge regarding social interaction mechanisms. To address these limitations, this paper introduces SIGRL, a Sociologically-Informed Graph Reresentation Learning for predicting social influence. SIGRL begins by randomly sampling a user's ego network as input. Drawing on theoretical models, we design a novel message-passing mechanism based on the bounded confidence model (BCM), refining the influence weights of local nodes during message propagation. Kolmogorov-Arnold Networks (KANs) are subsequently used to update node features, enhancing both the model's expressiveness. The theoretical model is further redefined as an Ordinary Differential Equation (ODE), incorporated as a constraint during neural network training. Extensive experiments on the Open Academic Graph (OAG), Twitter, Weibo, and Digg datasets demonstrate that SIGRL significantly outperforms existing baseline methods.