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

2025-02-01

AI Summary

This work addresses the problem of modeling primate social structures by identifying long-term stable animal subgroups—such as chimpanzee social clusters—from continuous, fine-grained proximity observations. We propose a dynamic weighted temporal network modeling framework: first, multi-source proximity signals are fused, and weights are optimized via a structurally and temporally consistent, interpretable, and learnable loss function; second, cluster persistence is rigorously assessed using significance testing to ensure robust community detection. The method demonstrates robustness on synthetic benchmarks and successfully recovers expert-validated, long-lasting social clusters in real chimpanzee proximity data. Compared to existing approaches, it substantially improves both the interpretability and statistical reliability of inferred dynamic social structures, enabling principled, hypothesis-driven analysis of primate social evolution.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Social Networks and Social Media: Computational social science

Abstract

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 across time. 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.

Problem

• Social Structure
• Animal Groups
• Chimpanzee Interaction

Innovation

• Temporal Social Networks
• Weighted Graph Representation
• Consistent Clustering