Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design

TL;DR

This paper introduces a scalable, gradient-based method to optimize social network interventions aimed at reducing polarization, effectively handling large-scale problems with complex objectives.

Contribution

We propose a novel hypergradient-based optimization framework for social network interventions that efficiently manages large-scale, non-convex problems to reduce polarization.

Findings

Successfully optimized interventions on networks with 3 million variables

Outperformed existing methods in computation time

Achieved significant polarization reduction

Abstract

Although social networks have expanded the range of ideas and information accessible to users, they are also criticized for amplifying the polarization of user opinions. Given the inherent complexity of these phenomena, existing approaches to counteract these effects typically rely on handcrafted algorithms and heuristics. We propose an elegant solution: we act on the network weights that model user interactions on social networks (e.g., frequency of communication), to optimize a performance metric (e.g., polarization reduction), while users' opinions follow the classical Friedkin-Johnsen model. Our formulation gives rise to a challenging large-scale optimization problem with non-convex constraints, for which we develop a gradient-based algorithm. Our scheme is simple, scalable, and versatile, as it can readily integrate different, potentially non-convex, objectives. We demonstrate its merit by: (i) rapidly solving complex social network intervention problems with 3 million variables based on the Reddit and DBLP datasets; (ii) significantly outperforming competing approaches in terms of both computation time and disagreement reduction.