## 6 Conclusions  
This paper proposes a novel approach called Hybrid Community-based Gravity Centrality (HCGC) for identifying hubs in transportation networks by employing local modularity, community detection, and gravity-based modeling. According to evaluation results on nine public real-world networks, HCGC outperforms existing approaches for identifying influential nodes in complex networks across different aspects. HCGC with a truncation radius \(R=2\) shows the high values of Kendall's correlation coefficient with SIR models on six out of nine networks. However, HCGC with a truncation radius \(R=3\) presents high values of the coefficient with SIR models on five out of nine networks compared to the traditional approaches. Additionally, HCGC with both truncation radii also achieves the best average imprecision function scores across all nine public real-world networks. This can be explained by the fact that our approach is outstanding in incorporating spreading influence that captures diffusion patterns in transportation networks. In addition to the public networks dataset, we present the ThaiNet dataset consisting of two comprehensive regional transportation networks in Thailand including the province network and district network constructed by using an adjacency matrix of the nation's provinces and districts, respectively. It represents Thailand's transportation network across 77 provinces and 928 districts in Thailand. Once applying HCGC to this ThaiNet dataset, HCGC shows high variance scores compared to existing network centrality measures (such as DC or CC) that possibly produces the same scores for multiple nodes. This allows the nodes in the network to be more easily ranked and identified as hubs with accurate scores. Based on the application of HCGC to the province network, provinces across six diverse regions can be considered hubs in the network. Lopburi is considered a hub in the central region with the top scores among different provinces in Thailand followed by Khon Kaen and Nakhon Ratchasima in the northeastern region. Phetchabun and Tak are two provinces in the northern region that achieve the highest scores in the region and are listed in the top ten provinces with the highest HCGC score. In addition, HCGC was applied to the district network. The districts that are listed in the top ten districts of high HCGC score represent the highly potential hubs in Thailand. All these districts are located in Bangkok province which is the capital of Thailand. The first rank district ordered  
Figure 9: The nodes in the first community which contains the vital hubs in the network. by HCGC is Pathum Wan followed by Thon Buri and Dusit, respectively. Furthermore, the communities for both networks are determined by the Louvain algorithm. There are six communities in the province network and thirty-two communities in the district network that can be categorized. These communities can be used to indicate vital nodes that have high potential to be hubs of Thailand and can visualize the size and location of each node in the map. Our proposed method and application demonstration provide not only in-depth geographical and economic analysis but also a strong foundation for policy-making aimed at bolstering national connectivity and development. Moreover, the adaptable methodology proposed here can be applied to other geographical areas, enabling systematic data collection and analysis worldwide. As such, the datasets and methods developed through this study are invaluable resources that can drive informed decision-making and stimulate socio-economic progress across various regions. Even though this study shows significant improvements over existing approaches with strong methodological and empirical contributions, there are limitations and opportunities for improvement. First, the transportation networks in the ThaiNet dataset were modeled as unweighted binary networks for simplicity, which may not reflect real-world systems that incorporate weighted factors such as distance, traffic volume, or speed.