# Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions

arXiv: https://arxiv.org/abs/2502.00302 · HTML: https://mlsanity.com/paper/2502.00302/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 54 references that are in the map.
- First posted on arXiv 1 Feb 2025.

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

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- [Multilayer Networks](https://mlsanity.com/paper/1309.7233/) (2013)
- [Multi-graph Fusion for Multi-view Spectral Clustering](https://mlsanity.com/paper/1909.06940/) (2019)
- [Network structure from rich but noisy data](https://mlsanity.com/paper/1703.07376/) (2017)
- [From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles](https://mlsanity.com/paper/1706.04370/) (2017)
- [Continuous-time Graph Representation with Sequential Survival Process](https://mlsanity.com/paper/2312.13068/) (2023)
- [Robust Angular Synchronization via Directed Graph Neural Networks](https://mlsanity.com/paper/2310.05842/) (2023)
- [The Strength of Friendship Ties in Proximity Sensor Data](https://mlsanity.com/paper/1401.5836/) (2014)
- [A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model](https://mlsanity.com/paper/1506.06696/) (2015)
- [A guide to choosing and implementing reference models for social network analysis](https://mlsanity.com/paper/2012.04720/) (2020)
- [Stability of the personal relationship networks in a longitudinal study of middle school students](https://mlsanity.com/paper/2303.03786/) (2023)

## Abstract (arXiv)

How can we identify groups of primate individuals which could be conjectured to drive social structure? To address this question, one of us has collected a time series of data for social interactions between chimpanzees. Here we use a network representation, leading to the task of combining these data into a time series of a single weighted network per time stamp, where different proximities should be given different weights reflecting their relative importance. We optimize these proximity-type weights in a principled way, using an innovative loss function which rewards structural consistency for consecutive time steps. The approach is empirically validated by carefully designed synthetic data. Using statistical tests, we provide a way of identifying groups of individuals that stay related for a significant length of time. Applying the approach to the chimpanzee data set, we detect cliques in the animal social network time series, which can be validated by real-world intuition from prior research and qualitative observations by chimpanzee experts.