# Reconstructing signed relations from interaction data

arXiv: https://arxiv.org/abs/2209.03219 · HTML: https://mlsanity.com/paper/2209.03219/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Cooperative Game Theory Approaches for Network Partitioning](https://mlsanity.com/paper/1707.03587/) (2017)
- [Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys](https://mlsanity.com/paper/1506.03645/) (2015)
- [Hierarchical Block Structures and High-resolution Model Selection in Large Networks](https://mlsanity.com/paper/1310.4377/) (2013)
- [Network structure from rich but noisy data](https://mlsanity.com/paper/1703.07376/) (2017)
- [Configuring Random Graph Models with Fixed Degree Sequences](https://mlsanity.com/paper/1608.00607/) (2016)
- [Network Backboning with Noisy Data](https://mlsanity.com/paper/1701.07336/) (2017)
- [Negative Link Prediction in Social Media](https://mlsanity.com/paper/1412.2723/) (2014)
- [From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles](https://mlsanity.com/paper/1706.04370/) (2017)
- [Generalised hypergeometric ensembles of random graphs: the configuration model as an urn problem](https://mlsanity.com/paper/1810.06495/) (2018)
- [Detecting social (in)stability in primates from their temporal co-presence network](https://mlsanity.com/paper/1909.09090/) (2019)

## Abstract (arXiv)

Positive and negative relations play an essential role in human behavior and shape the communities we live in. Despite their importance, data about signed relations is rare and commonly gathered through surveys. Interaction data is more abundant, for instance, in the form of proximity or communication data. So far, though, it could not be utilized to detect signed relations. In this paper, we show how the underlying signed relations can be extracted with such data. Employing a statistical network approach, we construct networks of signed relations in four communities. We then show that these relations correspond to the ones reported in surveys. Additionally, the inferred relations allow us to study the homophily of individuals with respect to gender, religious beliefs, and financial backgrounds. We evaluate the importance of triads in the signed network to study group cohesion.