Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks

• URL: http://arxiv.org/abs/2601.03062v1
• Date: Tue, 06 Jan 2026 14:45:43 GMT
• Title: Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
• Authors: Qusai Khaled, Pasquale De Marinis, Moez Louati, David Ferras, Laura Genga, Uzay Kaymak,
• Abstract summary: Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in sensor data.<n>We propose an explainable GNN framework that integrates mutual information to identify critical network regions.<n>Our fuzzy graph neural network (FGENConv) achieved Graph F1 scores of 0.889 for detection and 0.814 for localization.
• Score: 3.249485481362347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at capturing spatial-temporal dependencies in sensor data, their black-box nature and the limited work on graph-based explainable models for water networks hinder practical adoption. We propose an explainable GNN framework that integrates mutual information to identify critical network regions and fuzzy logic to provide clear, rule-based explanations for node classification tasks. After benchmarking several GNN architectures, we selected the generalized graph convolution network (GENConv) for its superior performance and developed a fuzzy-enhanced variant that offers intuitive explanations for classified leak locations. Our fuzzy graph neural network (FGENConv) achieved Graph F1 scores of 0.889 for detection and 0.814 for localization, slightly below the crisp GENConv 0.938 and 0.858, respectively. Yet it compensates by providing spatially localized, fuzzy rule-based explanations. By striking the right balance between precision and explainability, the proposed fuzzy network could enable hydraulic engineers to validate predicted leak locations, conserve human resources, and optimize maintenance strategies. The code is available at github.com/pasqualedem/GNNLeakDetection.

Related papers

• Graph Signal Generative Diffusion Models [74.75869068073577]
  We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for graph signal generation using denoising diffusion processes.<n>The architecture learns node features at different resolutions with skip connections between the encoder and decoder paths.<n>We demonstrate the effectiveness of the diffusion model in probabilistic forecasting of stock prices.
  arXiv  Detail & Related papers  (2025-09-21T21:57:27Z)
• Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network [8.318363497010969]
  This paper studies the application of GNNs to identify the specific types of network flows in an unsupervised manner. To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
  arXiv  Detail & Related papers  (2024-03-03T12:34:13Z)
• BLIS-Net: Classifying and Analyzing Signals on Graphs [20.345611294709244]
  Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. We introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. We show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
  arXiv  Detail & Related papers  (2023-10-26T17:03:14Z)
• Explainable Spatio-Temporal Graph Neural Networks [16.313146933922752]
  We propose an Explainable Spatio-Temporal Graph Neural Networks (STGNN) framework that enhances STGNNs with inherent explainability. Our framework integrates a unified-temporal graph attention network with a positional information fusion layer as the STG encoder and decoder. We demonstrate that STExplainer outperforms state-of-the-art baselines in terms of predictive accuracy and explainability metrics.
  arXiv  Detail & Related papers  (2023-10-26T04:47:28Z)
• Deep Graph Neural Networks via Posteriori-Sampling-based Node-Adaptive Residual Module [65.81781176362848]
  Graph Neural Networks (GNNs) can learn from graph-structured data through neighborhood information aggregation. As the number of layers increases, node representations become indistinguishable, which is known as over-smoothing. We propose a textbfPosterior-Sampling-based, Node-distinguish Residual module (PSNR).
  arXiv  Detail & Related papers  (2023-05-09T12:03:42Z)
• Graph Neural Network-Based Anomaly Detection for River Network Systems [0.8399688944263843]
  Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data. This paper presents a solution to the challenging task of anomaly detection for river network sensor data.
  arXiv  Detail & Related papers  (2023-04-19T01:32:32Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks [0.0]
  This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning. We present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process.
  arXiv  Detail & Related papers  (2022-07-14T10:59:39Z)
• Discriminability of Single-Layer Graph Neural Networks [172.5042368548269]
  Graph neural networks (GNNs) have exhibited promising performance on a wide range of problems. We focus on the property of discriminability and establish conditions under which the inclusion of pointwise nonlinearities to a stable graph filter bank leads to an increased discriminative capacity for high-eigenvalue content.
  arXiv  Detail & Related papers  (2020-10-17T18:52:34Z)
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [85.0332394224503]
  We study whether Graph Convolutional Networks (GCNs) can optimally integrate node features and topological structures in a complex graph with rich information. We propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN) Our experiments show that AM-GCN extracts the most correlated information from both node features and topological structures substantially.
  arXiv  Detail & Related papers  (2020-07-05T08:16:03Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)
• Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks [183.97265247061847]
  We leverage graph signal processing to characterize the representation space of graph neural networks (GNNs) We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. We also study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.
  arXiv  Detail & Related papers  (2020-03-08T13:02:15Z)

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.