mlsanityData to 14 Oct 2026

A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks

arXiv 2410.21006 ↗First posted 28 Oct 2024cs.LG cs.SY eess.SYcited by 0

Not on the map. Why?

Abstract (arXiv)

The development of Internet of Things (IoT) technologies has led to the widespread adoption of monitoring networks for a wide variety of applications, such as smart cities, environmental monitoring, and precision agriculture. A major research focus in recent years has been the development of graph-based techniques to improve the quality of data from sensor networks, a key aspect for the use of sensed data in decision-making processes, digital twins, and other applications. Emphasis has been placed on the development of machine learning and signal processing techniques over graphs, taking advantage of the benefits provided by the use of structured data through a graph topology. Many technologies such as the graph signal processing (GSP) or the successful graph neural networks (GNNs) have been used for data quality enhancement tasks. In this survey, we focus on graph-based models for data quality control in monitoring sensor networks. Furthermore, we delve into the technical details that are commonly leveraged for providing powerful graph-based solutions for data quality tasks in sensor networks, including missing value imputation, outlier detection, or virtual sensing. To conclude, we have identified future trends and challenges such as graph-based models for digital twins or model transferability and generalization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 103 of its 215 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

103 of 215 references.

References · 103

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  5. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  6. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,089
  7. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  8. 2019Simplifying Graph Convolutional Networkscited by 1,477
  9. 2016Variational Graph Auto-Encoderscited by 1,382
  10. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  11. 2015Gated Graph Sequence Neural Networkscited by 796
  12. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
38 more
  1. 2019Diffusion Improves Graph Learningcited by 414
  2. 2017Graph Signal Processing: Overview, Challenges and Applicationscited by 413
  3. 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 383
  4. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  5. 2015Diffusion-Convolutional Neural Networkscited by 268
  6. 2015Deep Convolutional Networks on Graph-Structured Datacited by 246
  7. 2018Techniques for Interpretable Machine Learningcited by 238
  8. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  9. 2017CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filterscited by 203
  10. 2021BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximationcited by 195
  11. 2022Rethinking Graph Neural Networks for Anomaly Detectioncited by 187
  12. 2018Adversarial Attack and Defense on Graph Data: A Surveycited by 167
  13. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  14. 2016How to learn a graph from smooth signalscited by 146
  15. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  16. 2021Graph Learning: A Surveycited by 141
  17. 2020DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphscited by 139
  18. 2019Graph Wavelet Neural Networkcited by 131
  19. 2020Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A surveycited by 124
  20. 2017GraphGAN: Graph Representation Learning with Generative Adversarial Netscited by 121
  21. 2018Learning graphs from data: A signal representation perspectivecited by 119
  22. 2020Graphon Neural Networks and the Transferability of Graph Neural Networkscited by 118
  23. 2018Connecting the Dots: Identifying Network Structure via Graph Signal Processingcited by 111
  24. 2019Meta-GNN: On Few-shot Node Classification in Graph Meta-learningcited by 105
  25. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  26. 2020Unifying Graph Convolutional Neural Networks and Label Propagationcited by 98
  27. 2021Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networkscited by 95
  28. 2020Transfer Learning of Graph Neural Networks with Ego-graph Information Maximizationcited by 93
  29. 2019Transferability of Spectral Graph Convolutional Neural Networkscited by 92
  30. 2020Inductive Graph Neural Networks for Spatiotemporal Krigingcited by 91
  31. 2019Machine Learning at the Network Edge: A Surveycited by 84
  32. 2021Graph-based Semi-supervised Learning: A Comprehensive Reviewcited by 79
  33. 2013Discrete Signal Processing on Graphs: Frequency Analysiscited by 73
  34. 2020Graph signal processing for machine learning: A review and new perspectivescited by 73
  35. 2019Quantum Graph Neural Networkscited by 66
  36. 2022Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observationscited by 64
  37. 2020Graph Kernels: State-of-the-Art and Future Challengescited by 63
  38. 2019Missing Data Imputation with Adversarially-trained Graph Convolutional Networkscited by 54

50 most cited of 103 All 103 (JSON)

Built on by

Not cited yet.