# Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model

arXiv: https://arxiv.org/abs/2301.09507 · HTML: https://mlsanity.com/paper/2301.09507/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 23 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Community Interaction and Conflict on the Web](https://mlsanity.com/paper/1803.03697/) (2018)
- [Signed Graph Attention Networks](https://mlsanity.com/paper/1906.10958/) (2019)
- [SDGNN: Learning Node Representation for Signed Directed Networks](https://mlsanity.com/paper/2101.02390/) (2021)
- [Exploiting Social Network Structure for Person-to-Person Sentiment Analysis](https://mlsanity.com/paper/1409.2450/) (2014)
- [A Review of Dynamic Network Models with Latent Variables](https://mlsanity.com/paper/1711.10421/) (2017)
- [Distributed Representations of Signed Networks](https://mlsanity.com/paper/1702.06819/) (2017)
- [A Long-Term Analysis of Polarization on Twitter](https://mlsanity.com/paper/1703.02769/) (2017)
- [POLE: Polarized Embedding for Signed Networks](https://mlsanity.com/paper/2110.09899/) (2021)
- [Finding large balanced subgraphs in signed networks](https://mlsanity.com/paper/2002.00775/) (2020)
- [Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations](https://mlsanity.com/paper/2212.12345/) (2022)
- [A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations](https://mlsanity.com/paper/2204.05885/) (2022)
- [HM-LDM: A Hybrid-Membership Latent Distance Model](https://mlsanity.com/paper/2206.03463/) (2022)

## Built on by (most cited first, 9 of 9)

- [A Survey on Archetypal Analysis](https://mlsanity.com/paper/2504.12392/) (2025)
- [Continuous-time Graph Representation with Sequential Survival Process](https://mlsanity.com/paper/2312.13068/) (2023)
- [Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings](https://mlsanity.com/paper/2409.10452/) (2024)
- [The Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks](https://mlsanity.com/paper/2503.03904/) (2025)
- [Aitchison Embeddings for Learning Compositional Graph Representations](https://mlsanity.com/paper/2605.00716/) (2026)
- [Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models](https://mlsanity.com/paper/2605.11142/) (2026)
- [A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks](https://mlsanity.com/paper/2308.15293/) (2023)
- [LIT-LVM: Structured Regularization for Interaction Terms in Linear Predictors using Latent Variable Models](https://mlsanity.com/paper/2506.15492/) (2025)
- [Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls](https://mlsanity.com/paper/2602.21342/) (2026)

## Abstract (arXiv)

Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typically, these representation learning approaches embed the networks into a low-dimensional space in which the role of each individual can be characterized in terms of their latent position. A major current concern in social networks is the emergence of polarization and filter bubbles promoting a mindset of "us-versus-them" that may be defined by extreme positions believed to ultimately lead to political violence and the erosion of democracy. Such polarized networks are typically characterized in terms of signed links reflecting likes and dislikes. We propose the latent Signed relational Latent dIstance Model (SLIM) utilizing for the first time the Skellam distribution as a likelihood function for signed networks and extend the modeling to the characterization of distinct extreme positions by constraining the embedding space to polytopes. On four real social signed networks of polarization, we demonstrate that the model extracts low-dimensional characterizations that well predict friendships and animosity while providing interpretable visualizations defined by extreme positions when endowing the model with an embedding space restricted to polytopes.