MS
M. Sabbaqi
4 records found
1
Inference of time varying data over graphs is of importance in real-world applications such as urban water networks, economics, and brain recordings. It typically relies on identifying a computationally affordable joint spatiotemporal method that can leverage the patterns in the
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In this paper, we propose a topology-aware Kalman filter for hidden dynamics over simplicial complex. Specifically, we consider that the hidden dynamics of a system can be expressed as a simplicial process that respects the structure of the underlying network. And these dynamics
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Reconstructing missing values and removing noise from network-based multivariate time series requires developing graph-time regularizers capable of capturing their spatiotemporal behavior. However, current approaches based on joint spatiotemporal smoothness, diffusion, or variati
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Graph-Time Convolutional Neural Networks
Architecture and Theoretical Analysis
Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invar
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