Surpassing probabilistic based community detection in flow-based mobility networks

Abstract

Understanding community structures in flow-based mobility networks is critical for analysing regional integration patterns, yet existing methods face two key limitations: (1) current modularity optimization algorithms struggle with resolution limits, and (2) failure to combine both local and global community detection method in flow-based mobility networks. To address these gaps, this study develops a novel framework integrating surpassing probability theory with community detection. The surpassing probability-based Leiden method (SPBL) first reshuffles flow weights to overcome resolution limits in the Leiden algorithm, enabling identification of macro-communities. Next, the two-phase surpassing probability community detection (TPSPCD) algorithm systematically decomposes these communities into granular sub-communities while preserving critical anchor relationships. The framework further introduces an Aggregate Surpassing Degree (ASD) metric to quantify the relative strength of internal versus external community connections. Our results revealed distinct core-periphery patterns within flow-based mobility networks, with strong community cohesion around key node centres. This study concludes that the proposed community detection method effectively captures localized interactions in flow-based mobility networks. This work advances both the theory and application of community detection in flow-based mobility networks, offering planners actionable tools for regional development.