Learning to Fuse Temporal Proximity Networks: A Case Study in Chimpanzee Social Interactions
This work addresses the challenge of analyzing social structures in primates for researchers in animal behavior and network science, but it is incremental as it builds on existing network fusion techniques.
The authors tackled the problem of identifying persistent social groups in chimpanzee interactions by developing a method to fuse temporal proximity networks with optimized weights, validated on synthetic data and applied to real chimpanzee data to detect cliques that align with expert observations.
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.