Dhaval Patel, Dzung Phan, et al.
ICDE 2022
Modeling and optimizing cities is a challenging task due to their complex and interconnected nature. Graph topologies and Graph Neural Networks (GNN) offer a promising framework for representing cities, leveraging their inherent heterogeneity and dynamicity. However, implementing efficiently GNNs is complex as existing approaches struggle to uncover the underlying cause-effect relationships. To address this limitation, our work introduces a causal graph discovery mechanism capable of identifying the causal processes. We conducted experiments to evaluate the framework's effectiveness in accurately representing complex systems and its scalability to handle large-scale scenarios. Two case studies focusing on transportation and buildings in smarter cities were examined, and the results demonstrate the capabilities of our approach.
Dhaval Patel, Dzung Phan, et al.
ICDE 2022
Robert Baseman
TechConnect 2024
Raya Horesh, Muneeza Azmat, et al.
AGU Fall 2022
Pin-Yu Chen, Alkiviadis Mertzios, et al.
INFORMS 2023