As machine learning models evolve, simple data no longer meets the information requirements of complex models. Therefore, there is a need for more enriched data to effectively learn intricate patterns. Expanding the underlying information in data presents a nontrivial task as it frequently introduces more noise than useful information. Existing techniques primarily concentrate on treating data as feature vectors, often disregarding the usage of fine-grained global relational information within the data. These global relationships play a pivotal role in augmenting the discriminatory power of data for classification tasks. This chapter introduces a novel method for modeling behavior that incorporates low-noise, fine-grained, structural information. Motivated by the interatomic forces forming chemical molecules, the method captures the global relationships between attributes and models the interactions among internal attributes of behavior to construct a behavior molecule graph for each data point. This approach not only enhances the representational power of the data through structural information but also ensures the mutual isolation of behavior molecule graphs, effectively preventing the propagation of noise. Experimental evaluation on representative datasets from different domains demonstrates the superiority of our fine-grained behavior learning compared to state-of-the-art data classification methods.