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07/07/2023
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Time series are the primary data type used to record dynamic system meas...
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03/21/2023
Graph Kalman Filters
The well-known Kalman filters model dynamical systems by relying on stat...
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02/08/2023
Taming Local Effects in Graph-based Spatiotemporal Forecasting
Spatiotemporal graph neural networks have shown to be effective in time ...
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02/03/2023
Where and How to Improve Graph-based Spatio-temporal Predictors
This paper introduces a novel residual correlation analysis, called AZ-a...
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01/04/2023
Graph state-space models
State-space models constitute an effective modeling tool to describe mul...
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05/26/2022
Sparse Graph Learning for Spatiotemporal Time Series
Outstanding achievements of graph neural networks for spatiotemporal tim...
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04/23/2022
AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs
We present the first whiteness test for graphs, i.e., a whiteness test f...
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10/11/2021
Understanding Pooling in Graph Neural Networks
Inspired by the conventional pooling layers in convolutional neural netw...
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09/09/2019
Distance-Preserving Graph Embeddings from Random Neural Features
We present Graph Random Neural Features (GRNF), a novel embedding method...
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03/18/2019
Autoregressive Models for Sequences of Graphs
This paper proposes an autoregressive (AR) model for sequences of graphs...
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05/18/2018
Change Point Methods on a Sequence of Graphs
The present paper considers a finite sequence of graphs, e.g., coming fr...
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05/16/2018
Learning Graph Embeddings on Constant-Curvature Manifolds for Change Detection in Graph Streams
The space of graphs is characterized by a non-trivial geometry, which of...
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05/03/2018
Anomaly and Change Detection in Graph Streams through Constant-Curvature Manifold Embeddings
Mapping complex input data into suitable lower dimensional manifolds is ...
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06/21/2017