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 interindividual 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 flexibility of the neural network would also allow for its application to a wide variety of data with interacting components, such as vocal communication.

中文翻译：

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使用基于注意的神经网络对个体间关系进行非参数分析

过去十年间，在动物研究中广泛采用的社会网络分析使成对的个体间关系揭示了动物社会系统的全球特征模式。通常基于社交行为的几何接近度和/或频率（例如修饰）来绘制动物社交网络，但是个体之间关系的适当度量标准尚不清楚，尤其是当有关物种/数据的先验知识受到限制时。在这项研究中，研究人员使用具有注意机制的神经网络探索了个人之间关系的非参数分析，该神经网络在自然语言处理中起着核心作用。注意机制的高度可解释性和整个神经网络的灵活性允许自动检测原始数据中包含的个体间关系，而无需有关数据中包括哪些关系/类型的先验知识/假设。对于这些案例研究，分析了从模拟特工和真实的日本猕猴收集的三维位置数据。所提出的方法成功地恢复了模拟数据背后的潜在关系，并在真实数据中发现了女性主导的关系，这与先前关于猕猴社会结构的概括是一致的。所提出的方法没有利用日本猕猴特有的任何行为模式，研究人员可以将其用于其他动物的位置数据。