Paper Citation Record · LEDGER

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems

As of 15 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2502.12164.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.12164 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:27:03.023142Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T12:23:19.417703Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T07:59:40.066911Z

Reference resolution

38 of 38 outbound references displayed

verified exact1
verified fuzzy34
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 930c9301-0249-48ef-89be-eec99fed7068 · outbound

This paper cites Urbanization,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Urbanization,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.560565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1017cfab-9bc6-4929-b651-9619788c01c6 · outbound

This paper cites Traffic4cast at neurips 2021 - temporal and spatial few-shot transfer learning in gridded geo-spatial processes,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Traffic4cast at neurips 2021 - temporal and spatial few-shot transfer learning in gridded geo-spatial processes,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.549653Z

Source-reported events for the cited work

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Observation ef2c7f73-953a-4ce3-a8ec-a84cf8e0dabc · outbound

This paper cites Artificial intelligence techniques in smart grid: A survey,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Artificial intelligence techniques in smart grid: A survey,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.540261Z

Source-reported events for the cited work

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Observation f2231986-ee9f-496f-82fe-0931d6be65b2 · outbound

This paper cites Leak detection methods in water distribution networks: A comparative survey on artificial intelligence applications,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Leak detection methods in water distribution networks: A comparative survey on artificial intelligence applications,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.530123Z

Source-reported events for the cited work

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Observation ec8e65ca-fde2-4748-a478-0ed1d3a59acf · outbound

This paper cites Deep learning for critical infrastructure resilience,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Deep learning for critical infrastructure resilience,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.520044Z

Source-reported events for the cited work

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Observation 92e5a622-5a58-47c5-bf3c-890e8a9af15d · outbound

This paper cites Challenges, methods, data–a survey of machine learning in water distribution networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Challenges, methods, data–a survey of machine learning in water distribution networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.509201Z

Source-reported events for the cited work

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Observation fd325700-2eb0-44c1-817d-4f7cb8f14d79 · outbound

This paper cites Epanet 2.2 user’s manual, water infrastructure division,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Epanet 2.2 user’s manual, water infrastructure division,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.498265Z

Source-reported events for the cited work

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Observation ae933721-53a9-411e-83c2-a24fa1615583 · outbound

This paper cites Physics-informed graph neural networks for water distribution systems,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Physics-informed graph neural networks for water distribution systems,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.487516Z

Source-reported events for the cited work

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Observation d31c6133-1b0a-4a50-90f6-7090f3d501af · outbound

This paper cites A weighting strategy to improve water demand forecasting performance based on spatial correlation between multiple sensors,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems A weighting strategy to improve water demand forecasting performance based on spatial correlation between multiple sensors,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.475967Z

Source-reported events for the cited work

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Observation 5aed23a2-a41f-4009-8731-01f6e586644b · outbound

This paper cites Machine learning model and strategy for fast and accurate detection of leaks in water supply network,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Machine learning model and strategy for fast and accurate detection of leaks in water supply network,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.464456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 29d45e01-934d-43db-a2cc-0b934453aa33 · outbound

This paper cites Investigating the suitability of concept drift detection for detecting leakages in water distribution networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Investigating the suitability of concept drift detection for detecting leakages in water distribution networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.453755Z

Source-reported events for the cited work

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Observation 6c9e47a0-d4f3-4f1b-9944-af316abc7c13 · outbound

This paper cites Taking care of our drinking water: Dealing with sensor faults in water distribution networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Taking care of our drinking water: Dealing with sensor faults in water distribution networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.442729Z

Source-reported events for the cited work

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Observation db6b491e-4c40-4670-9e8e-501e4efe84a8 · outbound

This paper cites Lost in optimiza- tion of water distribution systems: Better call bayes,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Lost in optimiza- tion of water distribution systems: Better call bayes,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.431377Z

Source-reported events for the cited work

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Observation 81760b7b-e14c-4146-b7f9-9e5cc07b14b3 · outbound

This paper cites A machine- learning approach for monitoring water distribution networks (wdns),.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems A machine- learning approach for monitoring water distribution networks (wdns),

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.419665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 14295ca4-78b8-49d1-8982-38712383cca5 · outbound

This paper cites Spatial graph convolution neural networks for water distribution systems,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Spatial graph convolution neural networks for water distribution systems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.408382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3d6c028b-3cd1-4103-a7ba-50ecadc18a40 · outbound

This paper cites Reconstructing nodal pressures in water distribution systems with graph neural networks.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Reconstructing nodal pressures in water distribution systems with graph neural networks

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-08T12:27:03.072014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4857f92c-0a60-4a21-87b8-0a93fb187085 · outbound

This paper cites Graph neural networks for pressure estimation in water distribution systems,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Graph neural networks for pressure estimation in water distribution systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.397417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e42c639d-f3a7-451d-903e-d08576c8ca8b · outbound

This paper cites Graph neural networks for state estimation in water distribution systems: Application of supervised and semisupervised learning,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Graph neural networks for state estimation in water distribution systems: Application of supervised and semisupervised learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.387503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff0bb54a-9bb8-4062-8246-e5cad86864fc · outbound

This paper cites Spectral networks and locally connected networks on graphs,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Spectral networks and locally connected networks on graphs,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.377665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation da20a19b-ee9f-44d9-84f0-91590fe34399 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Semi-supervised classification with graph convolutional networks,

Reference 20

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Source-reported events for the cited work

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Observation fbb87198-f52d-4b32-859e-53eae2763aea · outbound

This paper cites Convolutional neural networks on graphs with fast localized spectral filtering,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Convolutional neural networks on graphs with fast localized spectral filtering,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.360852Z

Source-reported events for the cited work

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Observation 72435686-9cc0-4651-ad50-63006bac564d · outbound

This paper cites Deep Convolutional Networks on Graph-Structured Data.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Deep Convolutional Networks on Graph-Structured Data

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T12:27:02.964984Z

Source-reported events for the cited work

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Observation 060d60b1-5a00-4a22-bcef-bf4744ea8edf · outbound

This paper cites Cayleynets: Graph convolutional neural networks with complex rational spectral filters,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Cayleynets: Graph convolutional neural networks with complex rational spectral filters,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.350676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation daa2da56-452e-424e-8811-1e691af7a9de · outbound

This paper cites Adaptive graph convolutional neural networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Adaptive graph convolutional neural networks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.339799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8abdecf8-9531-4e9c-b9aa-acba5429dbd1 · outbound

This paper cites Inductive representation learning on large graphs,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Inductive representation learning on large graphs,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.328755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 39fbc0d0-0c9b-40ca-b426-01abcc02a5bf · outbound

This paper cites Geometric deep learning on graphs and manifolds using mixture model cnns,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Geometric deep learning on graphs and manifolds using mixture model cnns,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.316944Z

Source-reported events for the cited work

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Observation a8e53973-75b6-4362-86bd-c3a5c790c9f2 · outbound

This paper cites Large-scale learnable graph convolutional networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Large-scale learnable graph convolutional networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.305138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 641f058d-84dd-45b2-b594-920008153e80 · outbound

This paper cites Learning convolutional neural networks for graphs,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Learning convolutional neural networks for graphs,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.293676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a443b518-b219-4574-b656-67135f908378 · outbound

This paper cites How powerful are graph neural networks?.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems How powerful are graph neural networks?

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.282284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 043fd677-5c81-4407-a06e-ccc73a5fba8b · outbound

This paper cites Graph Attention Networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Graph Attention Networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.271083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9a19db6a-10c6-4eb8-a595-55687d02d7c9 · outbound

This paper cites The graph neural network model,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems The graph neural network model,

Reference 31

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Source-reported events for the cited work

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Observation ecfa0dea-ea90-4602-92fd-aa473b515016 · outbound

This paper cites Hammer, Learning with recurrent neural networks , ser.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Hammer, Learning with recurrent neural networks , ser

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.252601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7b61f447-7ebe-4f0f-ab04-30428ce3ab1e · outbound

This paper cites State estimation in water distribution system via diffusion on the edge space,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems State estimation in water distribution system via diffusion on the edge space,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.241061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d8b39b56-b545-400c-aa7d-e5d8832fc3a3 · outbound

This paper cites Towards transfer- able metamodels for water distribution systems with edge-based graph neural networks,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Towards transfer- able metamodels for water distribution systems with edge-based graph neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.227913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 418ba417-788a-4363-84f1-b9f1c478b064 · outbound

This paper cites Opf-hgnn: Generalizable heterogeneous graph neural networks for ac optimal power flow,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Opf-hgnn: Generalizable heterogeneous graph neural networks for ac optimal power flow,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.216313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 047d3562-6cda-42cf-8e53-d9a1d9a5a4fa · outbound

This paper cites Epanet- benchmarks.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Epanet- benchmarks

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.107129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 727a05e5-bf92-49d9-b1c9-a129c64c4d9a · outbound

This paper cites Waterbenchmarkhub,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems Waterbenchmarkhub,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.095932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2b4a0977-fe6c-4e18-8c85-083d833b032a · outbound

This paper cites An overview of the water network tool for resilience,.

Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems An overview of the water network tool for resilience,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:27:03.084576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 67b0bfba-d7fb-4add-be3d-aac725aa984d · inbound

AI Data Centers and the Water Use Feedback Loop cites this paper.

AI Data Centers and the Water Use Feedback Loop Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems

Reference 84

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:59:40.068854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.