Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models Jane Carlen∗1 , Jaume de Dios Pont˚2 , Cassidy Mentus˚2 , Shyr-Shea Chang2 , Stephanie Wang2 , and Mason A. Porter2 arXiv:1908.09440v1 [cs.SI] 26 Aug 2019 1 Data Science...
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Role Detection in Bicycle-Sharing Networks Using Multilayer Stochastic Block Models Jane Carlen∗1 , Jaume de Dios Pont˚2 , Cassidy Mentus˚2 , Shyr-Shea Chang2 , Stephanie Wang2 , and Mason A. Porter2 arXiv:1908.09440v1 [cs.SI] 26 Aug 2019 1 Data Science Initiative, University of California, Davis 2 Department of Mathematics, University of California, Los Angeles August 27, 2019 Abstract Urban spatial networks are complex systems with interdependent roles of neighbor- hoods and methods of transportation between them. In this paper, we classify docking stations in bicycle-sharing networks to gain insight into the spatial delineations of three major United States cities from human mobility dynamics. We propose novel time- dependent stochastic block models, with degree-heterogeneous blocks and either mixed or discrete block membership, which (1) detect the roles served by bicycle-sharing dock- ing stations and (2) describe the traffic within and between blocks of stations over the course o
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