Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design

2025-02-18

AI Summary

Social networks broaden information exposure yet exacerbate opinion polarization. To address this, we propose a differentiable network intervention framework grounded in the Friedkin-Johnsen opinion dynamics model: it minimizes collective opinion divergence via end-to-end optimization of user interaction weights (e.g., engagement frequencies). Our key contribution is the first incorporation of hypergradient optimization into social network intervention—enabling differentiable control under non-convex objectives and large-scale non-convex constraints, thereby overcoming limitations of heuristic design. Evaluated on Reddit and DBLP datasets, our method efficiently solves large-scale optimization problems involving up to 3 million variables. It achieves superior computational efficiency and significantly stronger polarization mitigation compared to state-of-the-art baselines.

Technology Category

• Search and Optimization: Non-convex Optimization
• Machine Learning: Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Optimize social network intervention strategies
• Reduce user opinion polarization
• Develop scalable gradient-based algorithm

Innovation

• Hypergradient-based network weight optimization
• Integration of non-convex objectives
• Scalable gradient-based algorithm design