1 code implementation • 30 Apr 2024 • Alessio Gravina, Daniele Zambon, Davide Bacciu, Cesare Alippi
Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e. g., social networks and physical systems are characterized by continuous dynamics and sporadic observations.
1 code implementation • 19 Feb 2024 • Giovanni De Felice, Andrea Cini, Daniele Zambon, Vladimir V. Gusev, Cesare Alippi
Virtual sensing techniques allow for inferring signals at new unmonitored locations by exploiting spatio-temporal measurements coming from physical sensors at different locations.
no code implementations • 24 Oct 2023 • Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
The conditioning can take the form of an architectural inductive bias on the neural forecasting architecture, resulting in a family of deep learning models called spatiotemporal graph neural networks.
1 code implementation • 7 Jul 2023 • Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan
In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.
no code implementations • 21 Mar 2023 • Cesare Alippi, Daniele Zambon
The well-known Kalman filters model dynamical systems by relying on state-space representations with the next state updated, and its uncertainty controlled, by fresh information associated with newly observed system outputs.
1 code implementation • NeurIPS 2023 • Andrea Cini, Ivan Marisca, Daniele Zambon, Cesare Alippi
Spatiotemporal graph neural networks have shown to be effective in time series forecasting applications, achieving better performance than standard univariate predictors in several settings.
no code implementations • 3 Feb 2023 • Daniele Zambon, Cesare Alippi
The proposed AZ-analysis constitutes a valuable asset for discovering and highlighting those space-time regions where the model can be improved with respect to performance.
no code implementations • 4 Jan 2023 • Daniele Zambon, Andrea Cini, Lorenzo Livi, Cesare Alippi
State-space models constitute an effective modeling tool to describe multivariate time series and operate by maintaining an updated representation of the system state from which predictions are made.
1 code implementation • NeurIPS 2023 • Andrea Cini, Daniele Zambon, Cesare Alippi
Outstanding achievements of graph neural networks for spatiotemporal time series analysis show that relational constraints introduce an effective inductive bias into neural forecasting architectures.
1 code implementation • 23 Apr 2022 • Daniele Zambon, Cesare Alippi
We present the first whiteness test for graphs, i. e., a whiteness test for multivariate time series associated with the nodes of a dynamic graph.
2 code implementations • 11 Oct 2021 • Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi, Cesare Alippi
Inspired by the conventional pooling layers in convolutional neural networks, many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs.
1 code implementation • ICLR 2021 • Benjamin Paassen, Daniele Grattarola, Daniele Zambon, Cesare Alippi, Barbara Eva Hammer
With this result, we hope to provide a firm theoretical basis for a next generation of time series prediction models.
1 code implementation • ICML 2020 • Daniele Zambon, Cesare Alippi, Lorenzo Livi
We present Graph Random Neural Features (GRNF), a novel embedding method from graph-structured data to real vectors based on a family of graph neural networks.
2 code implementations • 18 Mar 2019 • Daniele Zambon, Daniele Grattarola, Lorenzo Livi, Cesare Alippi
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models.
no code implementations • 18 May 2018 • Daniele Zambon, Cesare Alippi, Lorenzo Livi
Given a finite sequence of graphs, e. g., coming from technological, biological, and social networks, the paper proposes a methodology to identify possible changes in stationarity in the stochastic process generating the graphs.
1 code implementation • 16 May 2018 • Daniele Grattarola, Daniele Zambon, Cesare Alippi, Lorenzo Livi
A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs.
no code implementations • 3 May 2018 • Daniele Zambon, Lorenzo Livi, Cesare Alippi
The proposed methodology consists in embedding graphs into a geometric space and perform change detection there by means of conventional methods for numerical streams.
1 code implementation • 21 Jun 2017 • Daniele Zambon, Cesare Alippi, Lorenzo Livi
Graph representations offer powerful and intuitive ways to describe data in a multitude of application domains.