Flow-Based Community Search Approach for Functionally Cohesive Building Group Recognition: A Case Study on Commercial Complexes

Recognizing functionally cohesive building groups is crucial for urban analysis, geospatial intelligence, and smart city applications. Traditional methods rely heavily on geometric information and often overlook the functional and semantic coherence of buildings, leading to their incorrect recognition. To overcome these challenges, this study introduces a flow-based community search approach, which models morphological, functional, and spatial relationships with a graph-based representation. The approach consists of graph representation learning, flow-based community generation, and community quality assessment, enabling adaptive building group recognition based on both structural coherence and functional similarity. Experimental results on commercial complex recognition demonstrate that our approach consistently outperforms traditional methods, achieving an improvement of over 5.4% in F1 score compared to the second-best method. Furthermore, its strong performance on limited training datasets highlights its robustness. These findings establish the proposed approach as an effective and reliable tool for recognizing functionally cohesive building groups, with practical viability in urban planning and policy formulation.