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A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data.

, , and . ASONAM, page 689-696. ACM, (2015)

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A Lightweight Approach for Origin-Destination Matrix Anonymization., , , , , and . ESANN, (2021)Fast Approximated Betweenness Centrality of Directed and Weighted Graphs., , , and . COMPLEX NETWORKS (1), volume 812 of Studies in Computational Intelligence, page 52-65. Springer, (2018)Cluster-based Computation of Exact Betweenness Centrality in Large Undirected Graphs., , and . IEEE BigData, page 603-608. IEEE, (2019)A Comparative Evaluation of Urban Fabric Detection Techniques Based on Mobile Traffic Data., , and . ASONAM, page 689-696. ACM, (2015)A Signature-based Approach for Data-driven Analysis of the Inter-modal Demand Dynamics., , and . MT-ITS, page 1-6. IEEE, (2023)Characterizing and Removing Oscillations in Mobile Phone Location Data., , , and . WOWMOM, page 1-9. IEEE, (2019)REINFORCE: rapid augmentation of large-scale multi-modal transport networks for resilience enhancement., , and . Appl. Netw. Sci., 6 (1): 79 (2021)Scalability Analysis of Cluster-based Betweenness Computation in Large Weighted Graphs., , , and . IEEE BigData, page 4006-4015. IEEE, (2018)Contextual Data Integration for Bike-sharing Demand Prediction with Graph Neural Networks in Degraded Weather Conditions., , and . ITSC, page 5436-5441. IEEE, (2023)Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France., , and . CoRR, (2023)