Efforts toward a comprehensive description of behavior have indeed facilitated the development of representation-based approaches that utilize deep learning to capture behavioral information. As behavior complexity increases, the expressive power of these models reaches a bottleneck. We coin the term ``behavioral molecular structure" and propose a new model called the Behavioral Molecular Structure (BMS). The model characterizes behaviors at the atomic level, analogizes behavioral attributes to atoms, and concretizes interrelations at the granularity of atoms using graphs. Here, we design three different downstream tasks to test the performance of the BMS model on public datasets. Additionally, we provide a preliminary theoretical analysis demonstrating that the BMS model can offer effective expressiveness for complex behaviors.

翻译：致力于行为全面描述的努力确实促进了基于深度学习捕捉行为信息的表征方法的发展。随着行为复杂性的增加，这些模型的表达能力达到了瓶颈。我们提出"行为分子结构"这一术语，并构建了名为行为分子结构（BMS）的新模型。该模型在原子层级表征行为特征，将行为属性类比为原子，并通过图结构在原子粒度层面具体化相互关联。本文设计了三种不同的下游任务来测试BMS模型在公开数据集上的性能。此外，我们提供了初步理论分析，证明BMS模型能为复杂行为提供有效的表达能力。