Linear Opinion Dynamics Model with Higher-Order Interactions

TL;DR

This paper extends the Friedkin-Johnsen opinion dynamics model from graphs to hypergraphs to better capture higher-order social interactions, revealing significant impacts on opinion formation and polarization.

Contribution

It introduces a method to encode hypergraph group interactions into pairwise interactions, extending the model to hypergraphs, and provides algorithms for opinion evaluation on directed hypergraphs.

Findings

Higher-order interactions significantly influence opinion polarization.

The proposed algorithm efficiently evaluates opinions on real-world hypergraph data.

Group interactions alter steady-state opinions compared to pairwise models.

Abstract

Opinion dynamics is a central subject of computational social science, and various models have been developed to understand the evolution and formulation of opinions. Existing models mainly focus on opinion dynamics on graphs that only capture pairwise interactions between agents. In this paper, we extend the popular Friedkin-Johnsen model for opinion dynamics on graphs to hypergraphs, which describe higher-order interactions occurring frequently on real networks, especially social networks. To achieve this, based on the fact that for linear dynamics the multi-way interactions can be reduced to effective pairwise node interactions, we propose a method to decode the group interactions encoded in hyperedges by undirected edges or directed edges in graphs. We then show that higher-order interactions play an important role in the opinion dynamics, since the overall steady-state expressed opinion and polarization differ greatly from those without group interactions. We also provide an interpretation of the equilibrium expressed opinion from the perspective of the spanning converging forest, based on which we design a fast sampling algorithm to approximately evaluate the overall opinion and opinion polarization on directed weighted graphs. Finally, we conduct experiments on real-world hypergraph datasets, demonstrating the performance of our algorithm.