## 1 Introduction
[13] used a graph-based approach with POI density and a marginalized graph autoencoder to delineate urban functional zones. [PERSON] et al. [14] developed a multilayer network model to detect stable hubs in Beijing's taxi network by capturing spatial-temporal correlations. [PERSON] et al. [15] analyzed the results of a real-world road network of Beijing after removing one or two nodes and accounting for the weighted structure of the network using classic binary node properties and network functioning measurements. Other works, such as [PERSON] et al. [16] and [PERSON] et al. [17], focused on ridership-weighted models and supernetwork theory to reveal hierarchical and multimodal structures within transit systems. In addition, a multi-commodity, multi-modal hierarchical hub location problem under demand uncertainty was explored by incorporating fuzzy demand, which contributed to optimizing both transportation costs and time [18]. Another approach proposed a hub location model based on a branch-and-cut algorithm and intermodal transportation, which sought to overcome several traditional assumptions in hub location modeling [19]. Hub identification plays a critical role in understanding and managing complex networks, yet existing methods still face notable challenges. Centrality-based methods have been extensively applied across disciplines--including physics, biology, telecommunications, computer science, sociology, infrastructure, and transportation--to detect influential nodes [20; 21; 22; 23]. Common metrics such as degree, betweenness, and closeness centrality offer useful perspectives but often yield low-variance scores, limiting their ability to distinguish truly influential nodes. Moreover, these traditional metrics typically emphasize either local connections or global structure, frequently overlooking intermediate patterns and spatial relationships that are critical in real-world applications. As a result, they may fall short in capturing the complexity of regionally diverse transportation networks. To overcome these limitations, a novel approach for hub identification in transportation networks called _Hybrid Community-based Gravity Centrality (HCGC)_, along with an additional local transportation network dataset, is presented. The key contributions of this study can be summarized as follows:  
* We propose HCGC, a method for identifying influential nodes in various complex networks. HCGC not only calculates the importance scores of each node based on local connections but also considers the global structure of the network by integrating local centrality features, gravity-based spatial modeling, and community detection. Moreover, it provides high-variance scores that are straightforward to use for ranking influential nodes. * The ThaiNet dataset, representing Thailand's transportation networks across 77 provinces and 928 districts, is introduced. It is constructed by contiguity-based adjacency, enabling the realistic modeling of regional connectivity. * An application of HCGC to ThaiNet is demonstrated. HCGC is not only applicable to general datasets but is particularly well-suited for transportation networks. * Extensive experiments and comprehensive analysis between the proposed approach and traditional centrality measures are conducted on nine public networks and the ThaiNet dataset.