  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 can offer effective expressiveness for complex
behaviors.
