# Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design

arXiv: https://arxiv.org/abs/2502.12973 · HTML: https://mlsanity.com/paper/2502.12973/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits](https://mlsanity.com/paper/2510.00803/) (2025)
- [Socially-Aware Recommender Systems Mitigate Opinion Clusterization](https://mlsanity.com/paper/2601.02412/) (2026)
- [CGRL: Causal-Guided Representation Learning for Node-Level Out-of-Distribution Generalization](https://mlsanity.com/paper/2603.24304/) (2026)

## Abstract (arXiv)

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.