UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics

Pagani, A; Wei, Z; Silva, R; Guo, W; (2022) Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics. ACM Transactions on Internet Technology , 22 (1) , Article 21. 10.1145/3461838. Green open access

[thumbnail of 2002.05508.pdf]
Preview
Text
2002.05508.pdf - Accepted version

Download (3MB) | Preview

Abstract

Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.

Type: Article
Title: Neural Network Approximation of Graph Fourier Transform for Sparse Sampling of Networked Dynamics
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3461838
Publisher version: https://doi.org/10.1145/3461838
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Sampling theory, neural networks, graph fourier transform
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10138518
Downloads since deposit
9Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item