# Minimizing Polarization and Disagreement in Social Networks

arXiv: https://arxiv.org/abs/1712.09948 · HTML: https://mlsanity.com/paper/1712.09948/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2017.

## Built on by (most cited first, 16 of 16)

- [Simplified Graph Convolution with Heterophily](https://mlsanity.com/paper/2202.04139/) (2022)
- [Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models](https://mlsanity.com/paper/2309.06375/) (2023)
- [Rewiring What-to-Watch-Next Recommendations to Reduce Radicalization Pathways](https://mlsanity.com/paper/2202.00640/) (2022)
- [Leveraging Large Language Models for Collective Decision-Making](https://mlsanity.com/paper/2311.04928/) (2023)
- [Rebalancing Social Feed to Minimize Polarization and Disagreement](https://mlsanity.com/paper/2308.14486/) (2023)
- [Random Walks with Erasure: Diversifying Personalized Recommendations on Social and Information Networks](https://mlsanity.com/paper/2102.09635/) (2021)
- [Measuring and Controlling Divisiveness in Rank Aggregation](https://mlsanity.com/paper/2306.08511/) (2023)
- [Adversaries with Limited Information in the Friedkin--Johnsen Model](https://mlsanity.com/paper/2306.10313/) (2023)
- [Quantifying the Echo Chamber Effect: An Embedding Distance-based Approach](https://mlsanity.com/paper/2307.04668/) (2023)
- [Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits](https://mlsanity.com/paper/2510.00803/) (2025)
- [AI-Mediated Communication Can Steer Collective Opinion](https://mlsanity.com/paper/2605.16245/) (2026)
- [Bridging or Breaking: Impact of Intergroup Interactions on Religious Polarization](https://mlsanity.com/paper/2402.11895/) (2024)
- [Optimally Improving Cooperative Learning in a Social Setting](https://mlsanity.com/paper/2405.20808/) (2024)
- [H-NeiFi: Non-Invasive and Consensus-Efficient Multi-Agent Opinion Guidance](https://mlsanity.com/paper/2507.13370/) (2025)
- [PACIFIER: Pacing Opinion Depolarization via a Unified Graph Learning Framework](https://mlsanity.com/paper/2602.23390/) (2026)
- [Agent-based Modeling: Equilibrium, Echo Chambers, and Efficiency in Hybrid Coevolutionary Opinion Games](https://mlsanity.com/paper/2609.27639/) (2026)

## Abstract (arXiv)

The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: given n agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes em polarization and em disagreement simultaneously? This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the other's viewpoint of the world, and decrease overall levels of polarization? Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a network with O(n/ε2) edges that is a (1+ε) approximation to the optimum. For a fixed graph, we additionally show how to optimize our objective function over the agents' innate opinions in polynomial time. We perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both polarization and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a ~ 60,000-fold reduction in polarization and disagreement.