The paper introduces a graph neural network (GNN) that can locate multiple simultaneous leaks in water distribution networks without predefining the number of leaks or exhaustively enumerating all possible leak combinations. It is tested on two benchmark networks—the 31-node Hanoi and the 92-node NET3—under varied demand, sparse sensor coverage, and different leak magnitudes. The GNN outperforms traditional classifiers such as multilayer perceptron, support vector machine, K-nearest neighbors, and random forest by up to 12% and 20% in accuracy on the Hanoi and NET3 networks, respectively, demonstrating scalable performance.
arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.
The paper introduces N-GNAS, a Node-Level Graph Neural Architecture Search framework that selects distinct network architectures for different node subsets during feature updates. It incorporates a contrastive learning loss to better separate features across categories. Experiments on eight node and graph classification datasets show that N-GNAS outperforms existing GNAS methods and hand‑crafted GNNs, achieving 78.26% accuracy on CiteSeer.
arXiv:2606. 24978v1 Announce Type: new Abstract: Accurate particulate matter (PM) prediction is crucial for mitigating air pollution.
arXiv:2601. 23207v2 Announce Type: replace-cross Abstract: Understanding what graph neural networks can learn, especially their ability to learn to execute algorithms, remains a central theoretical challenge.
arXiv:2607. 19392v1 Announce Type: new Abstract: Arsenic contamination in groundwater presents a longstanding public health crisis in the United States, especially for households depending on private wells.
Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs. Deep neural networks have shown promise in improving simulation efficiency, especially graph neural networks (GNNs), which demonstrate great potential due to their flexibility with unstructured data.
arXiv:2607. 11672v1 Announce Type: new Abstract: Industrial design in fields such as vehicle and aerospace engineering often relies on large-scale numerical simulations to evaluate fluid dynamics performance, which can incur substantial computational costs.
In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing...
arXiv:2607. 26699v1 Announce Type: new Abstract: We investigate message-passing graph neural networks with random node features.
arXiv:2606. 19566v1 Announce Type: cross Abstract: Electric vehicle charging stations (EVCSs) can expose distribution feeders to cyberattacks.
arXiv:2606. 19825v1 Announce Type: new Abstract: Accurate prediction of dust source emissions is critical for mitigating the significant environmental and health hazards posed by dust storms.
arXiv:2607. 29293v1 Announce Type: new Abstract: Accurate fault location is critical for distribution network reliability.