Time-varying Signals Recovery via Graph Neural Networks

• URL: http://arxiv.org/abs/2302.11313v3
• Date: Sat, 12 Aug 2023 22:47:28 GMT
• Title: Time-varying Signals Recovery via Graph Neural Networks
• Authors: Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros
• Abstract summary: We propose a Time Graph Neural Network (TimeGNN) for the recovery of time-varying graph signals. Our algorithm uses an encoder-decoder architecture with a specialized loss composed of a mean squared error function and a Sobolev smoothness operator.
• Score: 6.206392817156767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series. Effectively capturing the spatio-temporal information in these signals is essential for the downstream tasks. Previous studies have used the smoothness of the temporal differences of such graph signals as an initial assumption. Nevertheless, this smoothness assumption could result in a degradation of performance in the corresponding application when the prior does not hold. In this work, we relax the requirement of this hypothesis by including a learning module. We propose a Time Graph Neural Network (TimeGNN) for the recovery of time-varying graph signals. Our algorithm uses an encoder-decoder architecture with a specialized loss composed of a mean squared error function and a Sobolev smoothness operator.TimeGNN shows competitive performance against previous methods in real datasets.

Related papers

• Gegenbauer Graph Neural Networks for Time-varying Signal Reconstruction [4.6210788730570584]
  Time-varying graph signals are a critical problem in machine learning and signal processing with broad applications. We propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach.
  arXiv  Detail & Related papers  (2024-03-28T19:29:17Z)
• Networked Time Series Imputation via Position-aware Graph Enhanced Variational Autoencoders [31.953958053709805]
  We design a new model named PoGeVon which leverages variational autoencoder (VAE) to predict missing values over both node time series features and graph structures. Experiment results demonstrate the effectiveness of our model over baselines.
  arXiv  Detail & Related papers  (2023-05-29T21:11:34Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• Space-Time Graph Neural Networks with Stochastic Graph Perturbations [100.31591011966603]
  Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
  arXiv  Detail & Related papers  (2022-10-28T16:59:51Z)
• Scalable Spatiotemporal Graph Neural Networks [14.415967477487692]
  Graph neural networks (GNNs) are often the core component of the forecasting architecture. In most pretemporal GNNs, the computational complexity scales up to a quadratic factor with the length of the sequence times the number of links in the graph. We propose a scalable architecture that exploits an efficient encoding of both temporal and spatial dynamics.
  arXiv  Detail & Related papers  (2022-09-14T09:47:38Z)
• Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations [11.486068333583216]
  This paper tackles the problem of learning effective models to reconstruct missing data points. We propose a class of attention-based architectures, that given a set of highly sparse observations, learn a representation for points in time and space. Compared to the state of the art, our model handles sparse data without propagating prediction errors or requiring a bidirectional model to encode forward and backward time dependencies.
  arXiv  Detail & Related papers  (2022-05-26T16:40:48Z)
• Learning Graph Structure from Convolutional Mixtures [119.45320143101381]
  We propose a graph convolutional relationship between the observed and latent graphs, and formulate the graph learning task as a network inverse (deconvolution) problem. In lieu of eigendecomposition-based spectral methods, we unroll and truncate proximal gradient iterations to arrive at a parameterized neural network architecture that we call a Graph Deconvolution Network (GDN) GDNs can learn a distribution of graphs in a supervised fashion, perform link prediction or edge-weight regression tasks by adapting the loss function, and they are inherently inductive.
  arXiv  Detail & Related papers  (2022-05-19T14:08:15Z)
• Efficient-Dyn: Dynamic Graph Representation Learning via Event-based Temporal Sparse Attention Network [2.0047096160313456]
  Dynamic graph neural networks have received more and more attention from researchers. We propose a novel dynamic graph neural network, Efficient-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure.
  arXiv  Detail & Related papers  (2022-01-04T23:52:24Z)
• Space-Time Graph Neural Networks [104.55175325870195]
  We introduce space-time graph neural network (ST-GNN) to jointly process the underlying space-time topology of time-varying network data. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs.
  arXiv  Detail & Related papers  (2021-10-06T16:08:44Z)
• Spatio-Temporal Graph Scattering Transform [54.52797775999124]
  Graph neural networks may be impractical in some real-world scenarios due to a lack of sufficient high-quality training data. We put forth a novel mathematically designed framework to analyze-temporal data.
  arXiv  Detail & Related papers  (2020-12-06T19:49:55Z)
• Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification [70.50751397870972]
  FastGAT is a method to make attention based GNNs lightweight by using spectral sparsification to generate an optimal pruning of the input graph. We experimentally evaluate FastGAT on several large real world graph datasets for node classification tasks.
  arXiv  Detail & Related papers  (2020-06-15T22:07:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.