UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs
This work addresses polarization in social media by providing a predictive model for opinion evolution, though it appears incremental as it builds on existing graph neural network and opinion dynamics methods.
The paper tackles the challenge of modeling opinion dynamics on graphs by developing UniGO, a unified graph neural network framework that integrates different opinion fusion rules and uses synthetic data pretraining. Experimental results show UniGO effectively captures complex opinion formation and predicts future evolution with strong generalization to real-world scenarios.
Polarization and fragmentation in social media amplify user biases, making it increasingly important to understand the evolution of opinions. Opinion dynamics provide interpretability for studying opinion evolution, yet incorporating these insights into predictive models remains challenging. This challenge arises due to the inherent complexity of the diversity of opinion fusion rules and the difficulty in capturing equilibrium states while avoiding over-smoothing. This paper constructs a unified opinion dynamics model to integrate different opinion fusion rules and generates corresponding synthetic datasets. To fully leverage the advantages of unified opinion dynamics, we introduces UniGO, a framework for modeling opinion evolution on graphs. Using a coarsen-refine mechanism, UniGO efficiently models opinion dynamics through a graph neural network, mitigating over-smoothing while preserving equilibrium phenomena. UniGO leverages pretraining on synthetic datasets, which enhances its ability to generalize to real-world scenarios, providing a viable paradigm for applications of opinion dynamics. Experimental results on both synthetic and real-world datasets demonstrate UniGO's effectiveness in capturing complex opinion formation processes and predicting future evolution. The pretrained model also shows strong generalization capability, validating the benefits of using synthetic data to boost real-world performance.