Non-Parametric Analysis of Inter-Individual Relations Using an Attention-Based Neural Network

Social network analysis, which has been widely adopted in animal studies over the past decade, enables the revelation of global characteristic patterns of animal social systems from pairwise inter-individual relations. Animal social networks are typically drawn based on geometric proximity and/or frequency of social behaviors (e.g., grooming), but the appropriate metric for inter-individual relationship is not clear, especially when prior knowledge on the species/data is limited. In this study, researchers explored a non-parametric analysis of inter-individual relations using a neural network with the attention mechanism, which plays a central role in natural language processing. The high interpretability of the attention mechanism and flexibility of the entire neural network allow for automatic detection of inter-individual relations included in the raw data, without requiring prior knowledge/assumptions about what modes/types of relations are included in the data. For these case studies, three-dimensional location data collected from simulated agents and real Japanese macaques were analyzed. The proposed method successfully recovered the latent relations behind the simulated data and discovered female-oriented relations in the real data, which are in accordance with previous generalizations about the macaque social structure. The proposed method does not exploit any behavioral patterns that are particular to Japanese macaques, and researchers can use it for location data of other animals. The exibility of the neural network would also allow for its application to a wide variety of data with interacting components, such as vocal communication.