Revealing the Community Structure of Urban Bus Networks: a Multi-view Graph Learning Approach

Despite great progress in enhancing the efficiency of public transport, one still cannot seamlessly incorporate structural characteristics into existing algorithms. Moreover, comprehensively exploring the structure of urban bus networks through a single-view modelling approach is limited. In this research, a multi-view graph learning algorithm (MvGL) is proposed to aggregate community information from multiple views of urban bus system. First, by developing a single-view graph encoder module, latent community relationships can be captured during learning node embeddings. Second, inspired by attention mechanism, a multi-view graph encoder module is designed to fuse node embeddings in different views, aims to perceive more community information of urban bus network comprehensively. Then, the community assignment can be updated by using a differentiable clustering layer. Finally, a well-defined objective function, which integrates node level, community level and graph level, can help improve the quality of community detection. Experimental results demonstrated that MvGL can effectively aggregate community information from different views and further improve the quality of community detection. This research contributes to the understanding the structural characteristics of public transport networks and facilitates their operational efficiency.