{"as_of":"2026-08-15T05:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ddf8bafcee60654016012d50f41cfdca317a2a4f324eee9f70b81c27bbe8ba0b","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:27:03.023142Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T12:23:19.417703Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T07:59:40.066911Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"cited_work":{"arxiv_id":"2502.12164","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.12164","snapshot_observed_at":"2026-07-04T07:59:40.066911Z","title":"Scalableandrobustphysics-informed graph neural networks for water distribution systems.arXiv preprint arXiv:2502.12164, 2025","venue":null,"work_id":"1e58eb5f-9881-4239-9d17-26bb2d46b95c","year":2025},"citing_paper":{"arxiv_id":"2606.21760","last_updated":"2026-06-19T21:21:17Z","snapshot_observed_at":"2026-08-11T23:54:20.211908Z","submitted_at":"2026-06-19T21:21:17Z","title":"AI Data Centers and the Water Use Feedback Loop","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-06-26T12:23:19.417703Z"},"links":{"cited_paper":"/paper/2502.12164","citing_paper":"/paper/2606.21760"},"observation_digest":"sha256:a3a0be00b54a00ddc9cbce9d42169dcf4a8455b8bb4996eb9b5aa21dd4c25727","observation_id":"67b0bfba-d7fb-4add-be3d-aac725aa984d","resolution":{"observed_at":"2026-07-04T07:59:40.068854Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.12164/citation-record","integrity":"/paper/2502.12164/integrity","json":"/paper/2502.12164/citation-record.json","paper":"/paper/2502.12164"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.556805Z","title":"Urbanization,","venue":null,"work_id":"a0db3a22-d633-4a81-950b-88067dd817a4","year":2018},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.889213Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:bbd100ab438765ca53d349f9aee12e01cef863d966663dc60adb7bede2669013","observation_id":"930c9301-0249-48ef-89be-eec99fed7068","resolution":{"observed_at":"2026-08-08T12:27:03.560565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.546489Z","title":"Traffic4cast at neurips 2021 - temporal and spatial few-shot transfer learning in gridded geo-spatial processes,","venue":null,"work_id":"ba246ba0-57ce-48ad-ba20-ca39bad7ec60","year":2021},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.893541Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:e235c546eb607b4f663b1dcc673d5c3037e117693a3cf74f83c9e8d1ff5ccddf","observation_id":"1017cfab-9bc6-4929-b651-9619788c01c6","resolution":{"observed_at":"2026-08-08T12:27:03.549653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.536888Z","title":"Artificial intelligence techniques in smart grid: A survey,","venue":null,"work_id":"eb12ea53-3dd9-449d-981e-9481b56f8c3e","year":2021},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.897406Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:5de0471cad5de69addfb0f398a3edbab7afc9f3168640c09a6b4609ee76cc352","observation_id":"ef2c7f73-953a-4ce3-a8ec-a84cf8e0dabc","resolution":{"observed_at":"2026-08-08T12:27:03.540261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.526930Z","title":"Leak detection methods in water distribution networks: A comparative survey on artificial intelligence applications,","venue":null,"work_id":"8218fd81-a26a-404d-9eca-277ddb77f969","year":2022},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.901176Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:4808a0139532ba10ffb5ddd864c82bb325d9dfa6ce5fc9ef4f1fe34fd8d7a8f6","observation_id":"f2231986-ee9f-496f-82fe-0931d6be65b2","resolution":{"observed_at":"2026-08-08T12:27:03.530123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.516311Z","title":"Deep learning for critical infrastructure resilience,","venue":null,"work_id":"efd00899-d20a-4ad0-bb0c-06b93c056a21","year":2019},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.905213Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:f259bd440173bbdc03f70e3462229e7eef292d36b4c2c4f2ca1bb783814c2674","observation_id":"ec8e65ca-fde2-4748-a478-0ed1d3a59acf","resolution":{"observed_at":"2026-08-08T12:27:03.520044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.505192Z","title":"Challenges, methods, data–a survey of machine learning in water distribution networks,","venue":null,"work_id":"228f0a96-5978-492d-815b-467990d95378","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.909389Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:d95d0892cb3ef480fd6b6330aae9a118a4884e1aa68e2fdaee52c78707e7696a","observation_id":"92e5a622-5a58-47c5-bf3c-890e8a9af15d","resolution":{"observed_at":"2026-08-08T12:27:03.509201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.494253Z","title":"Epanet 2.2 user’s manual, water infrastructure division,","venue":null,"work_id":"44e2089b-d385-4ac8-b309-2923711869d2","year":2020},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.913442Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:fc1711b8d04a87bee3ec3d4af9950d43f7ebd8c690042d53ed71f88b1e392e5d","observation_id":"fd325700-2eb0-44c1-817d-4f7cb8f14d79","resolution":{"observed_at":"2026-08-08T12:27:03.498265Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.483689Z","title":"Physics-informed graph neural networks for water distribution systems,","venue":null,"work_id":"ac1b49be-641b-4480-b512-65e14915c580","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.917042Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:47f1e1e98ea945fadbde1f095d7037b6f6fa9f35fd83b688463c28a10f34fdae","observation_id":"ae933721-53a9-411e-83c2-a24fa1615583","resolution":{"observed_at":"2026-08-08T12:27:03.487516Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.472106Z","title":"A weighting strategy to improve water demand forecasting performance based on spatial correlation between multiple sensors,","venue":null,"work_id":"bfe66c3d-324d-416d-9a98-dc6f1eaf330d","year":2023},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.920455Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:a8630da9c40fc82136920d869b9c9387a10a7f46903b2df9b61c218365bbd62c","observation_id":"d31c6133-1b0a-4a50-90f6-7090f3d501af","resolution":{"observed_at":"2026-08-08T12:27:03.475967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.460627Z","title":"Machine learning model and strategy for fast and accurate detection of leaks in water supply network,","venue":null,"work_id":"41823004-110c-4e26-8543-eeb165c625fb","year":2021},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.924130Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:403fdb288ddbbd38ea013ae5757ea28c50a09a476a3c7b23cf53e66f7343a8ba","observation_id":"5aed23a2-a41f-4009-8731-01f6e586644b","resolution":{"observed_at":"2026-08-08T12:27:03.464456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.449974Z","title":"Investigating the suitability of concept drift detection for detecting leakages in water distribution networks,","venue":null,"work_id":"54f6767a-3600-470f-b058-b3965db56783","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.927702Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:d86c8694fcfdc1d9a56760891a7ae50efa95e5e6e320384ce5577bb1734e4392","observation_id":"29d45e01-934d-43db-a2cc-0b934453aa33","resolution":{"observed_at":"2026-08-08T12:27:03.453755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.438713Z","title":"Taking care of our drinking water: Dealing with sensor faults in water distribution networks,","venue":null,"work_id":"57786cb6-7da1-4f75-a1e8-cbd71cc31ce5","year":2022},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.930848Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:83fec7f5d7884b0b1dd14091db25de9aec00467994a9e87c7b7544b0ac25688a","observation_id":"6c9e47a0-d4f3-4f1b-9944-af316abc7c13","resolution":{"observed_at":"2026-08-08T12:27:03.442729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.427430Z","title":"Lost in optimiza- tion of water distribution systems: Better call bayes,","venue":null,"work_id":"35bbbe23-a812-4e5a-b90d-4bd0d7cb32c7","year":2022},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.934089Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:063fc21836843e5d5c507a0d6717a17f188e95401bd4aa597bb8a552b048cbe7","observation_id":"db6b491e-4c40-4670-9e8e-501e4efe84a8","resolution":{"observed_at":"2026-08-08T12:27:03.431377Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.415745Z","title":"A machine- learning approach for monitoring water distribution networks (wdns),","venue":null,"work_id":"52195efa-cf93-4bea-b0bf-927ef68d4e29","year":2023},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.937164Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:89e33a6f93b0a473db20c3be11aaaf2e5853123c5d68722d19e5b7fa3ef4c19d","observation_id":"81760b7b-e14c-4146-b7f9-9e5cc07b14b3","resolution":{"observed_at":"2026-08-08T12:27:03.419665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.404192Z","title":"Spatial graph convolution neural networks for water distribution systems,","venue":null,"work_id":"da1f896a-dace-4ea0-b9d5-d46d7110098d","year":2023},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.940559Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:df39182292a56276a844ed1e7c9165b2adc95ff0c6898042a00ec21f80e7f71a","observation_id":"14295ca4-78b8-49d1-8982-38712383cca5","resolution":{"observed_at":"2026-08-08T12:27:03.408382Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.13619","last_updated":"2021-11-08T16:42:00Z","snapshot_observed_at":"2026-08-13T19:32:48.644888Z","submitted_at":"2021-04-28T07:56:55Z","title":"Reconstructing nodal pressures in water distribution systems with graph neural networks","version":2},"cited_work":{"arxiv_id":"2104.13619","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.13619","snapshot_observed_at":"2026-08-08T12:27:03.065547Z","title":"Reconstructing nodal pressures in water distribution systems with graph neural networks","venue":"cs.LG","work_id":"d3e37810-a6ce-49f1-af4e-f522a4f31c42","year":2021},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.943855Z"},"links":{"cited_paper":"/paper/2104.13619","citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:a54b0c0f6ede53c22b8beb5ab4cde5f00d3885eb2b065bafb2750e18931501aa","observation_id":"3d6c028b-3cd1-4103-a7ba-50ecadc18a40","resolution":{"observed_at":"2026-08-08T12:27:03.072014Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.393757Z","title":"Graph neural networks for pressure estimation in water distribution systems,","venue":null,"work_id":"5a81e7ef-407d-41c3-a132-2adac4654fd7","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.947491Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:21d0c7806755e85d574a82403547b7f3821841d0c3dcd7714c7ea9829083461e","observation_id":"4857f92c-0a60-4a21-87b8-0a93fb187085","resolution":{"observed_at":"2026-08-08T12:27:03.397417Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.384293Z","title":"Graph neural networks for state estimation in water distribution systems: Application of supervised and semisupervised learning,","venue":null,"work_id":"28d8b406-ba5b-4345-b211-bcdceffb5bf2","year":2022},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.950725Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:9e2c2807ef0bcdde2e2a62387476d7bfba36e1874ff70d8c8e1c570cdd1781f2","observation_id":"e42c639d-f3a7-451d-903e-d08576c8ca8b","resolution":{"observed_at":"2026-08-08T12:27:03.387503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.374440Z","title":"Spectral networks and locally connected networks on graphs,","venue":null,"work_id":"d0a6820e-ef35-4067-9369-dffd4c78f4e5","year":2014},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.954337Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:67862b5576337a99a4ef75731ed7cef89e4a5f83a836290dfe041468f3a25538","observation_id":"ff0bb54a-9bb8-4062-8246-e5cad86864fc","resolution":{"observed_at":"2026-08-08T12:27:03.377665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:02.957875Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.957875Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:8d35da9b4da8be13bf4167b2b41f96bb5ccdbbf85eefd4ee61d43b1359ee84b2","observation_id":"da20a19b-ee9f-44d9-84f0-91590fe34399","resolution":{"observed_at":"2026-08-08T12:27:02.957875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.357632Z","title":"Convolutional neural networks on graphs with fast localized spectral filtering,","venue":null,"work_id":"c63a6b7a-2c27-47a6-81b7-0e054962de1c","year":2016},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.961585Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:ec7df1ad92f5e8357ab8b5092c9cf477d92165e80aa87ef83ef50e22479a3afb","observation_id":"fbb87198-f52d-4b32-859e-53eae2763aea","resolution":{"observed_at":"2026-08-08T12:27:03.360852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1506.05163","last_updated":"2015-06-16T22:31:09Z","snapshot_observed_at":"2026-08-14T22:44:08.546380Z","submitted_at":"2015-06-16T22:31:09Z","title":"Deep Convolutional Networks on Graph-Structured Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.05163","snapshot_observed_at":"2026-08-08T12:27:02.964984Z","title":"Deep convolutional networks on graph-structured data,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.964984Z"},"links":{"cited_paper":"/paper/1506.05163","citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:4fb0ca828a920b8950537b87b85a3d6845512337fbe5871eb201920f796a2973","observation_id":"72435686-9cc0-4651-ad50-63006bac564d","resolution":{"observed_at":"2026-08-08T12:27:02.964984Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.346924Z","title":"Cayleynets: Graph convolutional neural networks with complex rational spectral filters,","venue":null,"work_id":"34b114d1-295b-4f9f-abac-feac2275ed18","year":2018},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.968972Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:8d2ab4bbdda76b62b72222446e1244de749c7e23e741c1965f388b83ab1f26e7","observation_id":"060d60b1-5a00-4a22-bcef-bf4744ea8edf","resolution":{"observed_at":"2026-08-08T12:27:03.350676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.336083Z","title":"Adaptive graph convolutional neural networks,","venue":null,"work_id":"dfd1baa3-3cd2-4ba8-b14d-214db0cd8a1e","year":2018},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.972562Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:2f48093c546369648a0d6d3a20d1e58e05d82aa4c95d89a9e2f3385fff19848d","observation_id":"daa2da56-452e-424e-8811-1e691af7a9de","resolution":{"observed_at":"2026-08-08T12:27:03.339799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.324541Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":"78d012ce-2c8a-415e-b0d5-4958e893f1d8","year":2017},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.976118Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:801d9bea959431aaa6833765468d1c255839b20dc2cb2631c903780dae1e3331","observation_id":"8abdecf8-9531-4e9c-b9aa-acba5429dbd1","resolution":{"observed_at":"2026-08-08T12:27:03.328755Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.313005Z","title":"Geometric deep learning on graphs and manifolds using mixture model cnns,","venue":null,"work_id":"ce595d40-3c49-452a-9958-d1ea0fb405c1","year":2017},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.979698Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:12ca6ab1daf2c93d04516a26d8ff11640048d44b78a1a926f3dfd732ad2f062f","observation_id":"39fbc0d0-0c9b-40ca-b426-01abcc02a5bf","resolution":{"observed_at":"2026-08-08T12:27:03.316944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.301317Z","title":"Large-scale learnable graph convolutional networks,","venue":null,"work_id":"a88cd39e-fba0-4e40-b380-eca16817e96b","year":2018},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.983268Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:0ae7c1ac0fa91ae2dbb41eaf37e182985edfaf1216d3a97c2b6bcfa51db31a41","observation_id":"a8e53973-75b6-4362-86bd-c3a5c790c9f2","resolution":{"observed_at":"2026-08-08T12:27:03.305138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.289786Z","title":"Learning convolutional neural networks for graphs,","venue":null,"work_id":"6216e332-2829-4408-ba0a-8e1afe869d9d","year":2016},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.986827Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:fc8481d6d1b9d2cf78e732e375d24385aae0cea6608740ddd035e54cc53511d5","observation_id":"641f058d-84dd-45b2-b594-920008153e80","resolution":{"observed_at":"2026-08-08T12:27:03.293676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.278430Z","title":"How powerful are graph neural networks?","venue":null,"work_id":"f2f9ccc4-489c-4db1-a672-d0ebd207d33e","year":2019},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.990715Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:057c899f295cfc08eaddc4cff5393b3f20bb7974d475b998f69b69f120bcb619","observation_id":"a443b518-b219-4574-b656-67135f908378","resolution":{"observed_at":"2026-08-08T12:27:03.282284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.267191Z","title":"Graph Attention Networks,","venue":null,"work_id":"d2933e4e-1853-4080-9e78-d2c717f94fb0","year":2018},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.994360Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:e5863ff371c0c0a60781cb82f8ef61341c5b24ba8728876fc8e1c566799ab96d","observation_id":"043fd677-5c81-4407-a06e-ccc73a5fba8b","resolution":{"observed_at":"2026-08-08T12:27:03.271083Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:02.998219Z","title":"The graph neural network model,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:02.998219Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:b493ee96579e39ed691964fce5a9be5931b28d454ed7b0f1fd0e7d0ee5882978","observation_id":"9a19db6a-10c6-4eb8-a595-55687d02d7c9","resolution":{"observed_at":"2026-08-08T12:27:02.998219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.248764Z","title":"Hammer, Learning with recurrent neural networks , ser","venue":null,"work_id":"0ad08278-b987-4534-88cc-5118895bc1e2","year":2000},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.001703Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:4fb79c72dfc88ee1ff8b1442c730c0918287bd6d9048c689e1a80d166100ebe2","observation_id":"ecfa0dea-ea90-4602-92fd-aa473b515016","resolution":{"observed_at":"2026-08-08T12:27:03.252601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.236692Z","title":"State estimation in water distribution system via diffusion on the edge space,","venue":null,"work_id":"919752c3-fbe8-4834-bc02-47ed00128517","year":2025},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.005167Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:612f9316d1df82b41bb50d3642c2532c3687c26506ef937481b7eb1ec003ecbf","observation_id":"7b61f447-7ebe-4f0f-ab04-30428ce3ab1e","resolution":{"observed_at":"2026-08-08T12:27:03.241061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.223575Z","title":"Towards transfer- able metamodels for water distribution systems with edge-based graph neural networks,","venue":null,"work_id":"f438ce1c-026b-4d35-8b1a-6ce00029ac1b","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.008716Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:030b2894a8049c815198f9f3742ead5f11ac2ebb8d2c89f4fa9033af8e76f3b4","observation_id":"d8b39b56-b545-400c-aa7d-e5d8832fc3a3","resolution":{"observed_at":"2026-08-08T12:27:03.227913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.113969Z","title":"Opf-hgnn: Generalizable heterogeneous graph neural networks for ac optimal power flow,","venue":null,"work_id":"83fbd792-b557-47a0-90ea-684a89bb46cd","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.012155Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:b05dc864919aec4f8849b31bc8bb83af3ccebbdfb09d98c251730f292e98bc27","observation_id":"418ba417-788a-4363-84f1-b9f1c478b064","resolution":{"observed_at":"2026-08-08T12:27:03.216313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.103634Z","title":"Epanet- benchmarks","venue":null,"work_id":"8e530180-ed0c-4f6e-89b8-45f706508830","year":null},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.015482Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:e3f7699b025eaed7d3aa88f702063e0736bcb9d259f0e2992469e61146d91713","observation_id":"047d3562-6cda-42cf-8e53-d9a1d9a5a4fa","resolution":{"observed_at":"2026-08-08T12:27:03.107129Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.092259Z","title":"Waterbenchmarkhub,","venue":null,"work_id":"e11a3425-7573-4ff3-9f22-c6169f4535bf","year":2024},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.019392Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:78edd3752a49570ceafb5e27bb55924f59d78ff590044fa8d0b07c0828e18ad6","observation_id":"727a05e5-bf92-49d9-b1c9-a129c64c4d9a","resolution":{"observed_at":"2026-08-08T12:27:03.095932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:27:03.080686Z","title":"An overview of the water network tool for resilience,","venue":null,"work_id":"f2514058-0408-4a05-96d9-fa4c3e91b19b","year":2018},"citing_paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T12:27:03.023142Z"},"links":{"citing_paper":"/paper/2502.12164"},"observation_digest":"sha256:762e7ab5e567ef65bfd3112fe271250ee5e53091d82265d446b06abc744caa31","observation_id":"2b4a0977-fe6c-4e18-8c85-083d833b032a","resolution":{"observed_at":"2026-08-08T12:27:03.084576Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.12164","last_updated":"2025-02-11T13:38:14Z","latest_version":1,"primary_category":"cs.NE","snapshot_observed_at":"2026-08-12T00:18:30.968173Z","submitted_at":"2025-02-11T13:38:14Z","title":"Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":1,"verified_fuzzy":34},"total_outbound_references":38},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"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."}