UniGO: A Unified Graph Neural Network for Modeling Opinion Dynamics on Graphs

TL;DR

UniGO is a graph neural network framework that models opinion evolution on social media graphs, integrating diverse opinion fusion rules, and leveraging synthetic data for improved real-world prediction accuracy.

Contribution

The paper introduces UniGO, a unified GNN framework that captures opinion dynamics, incorporates multiple fusion rules, and uses synthetic datasets for pretraining to enhance real-world applicability.

Findings

UniGO effectively models opinion evolution on synthetic and real datasets.

Pretraining on synthetic data improves real-world prediction performance.

UniGO mitigates over-smoothing while capturing equilibrium states.

Abstract

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.