{"as_of":"2026-08-10T05:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aab8e90489246345dfabd357902fcf304b09c42ed07b083e2a5368897e458ff2","coverage":[{"denominator":15,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:55:27.584622Z","state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.12425/citation-record","integrity":"/paper/2506.12425/integrity","json":"/paper/2506.12425/citation-record.json","paper":"/paper/2506.12425"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T00:55:30.021636Z","title":"Personalized subgraph federated learning","venue":null,"work_id":"7a8137c8-09fc-4148-afa7-0c0fa4be4c35","year":2023},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.436784Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:23aefacba03e3d716a057ae6f1df7dc2e2fd491c73e846301049c8b0102f30c4","observation_id":"937a8679-ba0a-4dc7-a940-40d9ef135428","resolution":{"observed_at":"2026-08-07T00:55:30.097608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:29.786567Z","title":"Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources.Journal of Parallel and Distributed Computing, 203:105103, 2025","venue":null,"work_id":"3d63c05d-e0aa-4c0f-84b5-4633cc160290","year":2025},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.521910Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:a6e48927d3497f94c676da82d417c587005eb756fbd5a2d835d5045d1f197ab1","observation_id":"e1cbbf90-19e2-4fec-a483-e1d8cd646f5b","resolution":{"observed_at":"2026-08-07T00:55:29.891111Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:29.574363Z","title":"Tifl: A tier-based federated learning system","venue":null,"work_id":"264d4c09-3749-4def-b321-5b61d65f59f7","year":2020},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.608872Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:3b87a75fd3a6b72b5b3f507d6e84bc1a2a8e49aa32ac2e8cc4b2fcc301ee52c9","observation_id":"237dd0b4-367f-4575-8b0c-4815d07ff70b","resolution":{"observed_at":"2026-08-07T00:55:29.668259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:29.282806Z","title":"Inductive representation learning on large graphs.Advances in Neural Information Processing Sys- tems, 2017","venue":null,"work_id":"900de8e2-84ab-4f4c-aa73-098eb3e9b53f","year":2017},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.740404Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:4688c2bb49c4858061ad3d454ea6c52053ce168fa5a8fa939fc5da749269ae72","observation_id":"ecc83786-b295-4dda-8707-32278f89320a","resolution":{"observed_at":"2026-08-07T00:55:29.402153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:29.070311Z","title":"METIS: A software package for par- titioning unstructured graphs, partitioning meshes, and computing fill- reducing orderings of sparse matrices, 1997","venue":null,"work_id":"9dacdd6e-2c76-492d-9c7b-692d561e99bb","year":1997},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.843510Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:c42e557a37dc86325ec42e4546aa987612c4dbd83a7de1f561f916a0a6773683","observation_id":"62f2a6a2-650c-487b-a0c0-637eef511f1f","resolution":{"observed_at":"2026-08-07T00:55:29.194040Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:29.011030Z","title":"Semi-supervised classification with graph convolutional networks.ArXiv, 2016","venue":null,"work_id":"a772e0db-defb-4833-ba5a-ccd64f435b16","year":2016},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.893813Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:7533514a44480acd22cee41d162bd0a983d41816c0fa868c84a0cdc1c6071550","observation_id":"4ecf5a3a-96a1-4274-8be2-233ea4d25b50","resolution":{"observed_at":"2026-08-07T00:55:29.056395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:28.799059Z","title":"Communication-efficient learning of deep networks from decentralized data","venue":null,"work_id":"b2ad30ef-ee2c-46ea-b920-e191e9d22137","year":2017},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:26.978643Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:87714283a3494ce9e485c949bb5db04704262ce603c3ae595866bb570acf3df3","observation_id":"fc7fa35c-a502-42a1-9d38-8210b2279dca","resolution":{"observed_at":"2026-08-07T00:55:28.897196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:28.636799Z","title":"Weisfeiler and leman go neural: Higher-order graph neural networks","venue":null,"work_id":"9846e5a2-652e-43b4-9c53-0f1a6bffec56","year":2019},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.059450Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:c0aa89ec566305244013b854c825458e29117c630cdc95d58e80ea1c72b9f4d3","observation_id":"9ad4b378-f0b5-4ff3-9c21-ee4c1a230432","resolution":{"observed_at":"2026-08-07T00:55:28.705670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:28.486182Z","title":"Deep graph library: Towards efficient and scalable deep learning on graphs","venue":null,"work_id":"07190023-de64-4aa2-b9fb-15863b7db0e7","year":2019},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.138806Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:19b8932a4fd0e24a59ba56c5da8c338a43617096f0ee128b773ffaa59ab46432","observation_id":"826c152b-00e4-467d-8461-f1168781fa5f","resolution":{"observed_at":"2026-08-07T00:55:28.534493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:28.332346Z","title":"Federatedscope-gnn: Towards a unified, com- prehensive and efficient package for federated graph learning","venue":null,"work_id":"e233bc2c-f62a-4b13-9b97-d3194491f2c8","year":2022},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.200018Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:acc3fb89a7738849eb499b4c9ea60dfb7af59bdaa13e8beb43d14e9d97345eb4","observation_id":"33ca4104-fe96-4694-a867-4aeedf7b221f","resolution":{"observed_at":"2026-08-07T00:55:28.416662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2102.04925","last_updated":"2021-03-01T08:27:46Z","snapshot_observed_at":"2026-08-10T02:14:16.124255Z","submitted_at":"2021-02-09T16:30:53Z","title":"FedGNN: Federated Graph Neural Network for Privacy-Preserving Recommendation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2102.04925","snapshot_observed_at":"2026-08-07T00:55:27.296486Z","title":"Fedgnn: Federated graph neural network for privacy-preserving recommen- dation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.296486Z"},"links":{"cited_paper":"/paper/2102.04925","citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:d84222c496691fa861d162f64b2e6c202af7bf938fac790bfd4b33acffbdd6ee","observation_id":"172c31b4-e7c5-47ef-a2a9-1c8e7afb358c","resolution":{"observed_at":"2026-08-07T00:55:27.296486Z","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":"2026-08-07T00:55:28.190087Z","title":"Embedding communication for federated graph neural networks with privacy guarantees","venue":null,"work_id":"3e7516ee-61b6-4757-8293-767fe92f74c0","year":2023},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.367858Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:8353b8651cea6e11cffd0da7ccf825a29bc76cbf7ad36fabae48b7ed4e973f32","observation_id":"60136711-02de-491e-9a5b-47075b2b3ace","resolution":{"observed_at":"2026-08-07T00:55:28.258597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:28.016630Z","title":"Federated graph classification overnon-iidgraphs","venue":null,"work_id":"afe8508b-dd2b-4a86-bd6a-86080a0219b2","year":2021},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.459997Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:a79b8f49025cb9f38031343b8b682ff8a4a9032ebf3b2497dc075d7d4ceef51b","observation_id":"22a96243-a8ad-4b55-9767-1894835857ba","resolution":{"observed_at":"2026-08-07T00:55:28.089380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:27.873279Z","title":"Fedgcn: Convergence-communication tradeoffs in federated training of graph convo- lutional networks","venue":null,"work_id":"fc818068-ef6a-4b07-a40b-c2e96cfb8d6f","year":2023},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.526635Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:934ca4594a9bf6be5f2ec8bb21a72a54cda03b88ab026c80806092dcfebd7d7c","observation_id":"f119ec63-118a-4542-bcf7-ef35e8dd42eb","resolution":{"observed_at":"2026-08-07T00:55:27.941989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-07T00:55:27.711865Z","title":"Sub- graph federated learning with missing neighbor generation","venue":null,"work_id":"9a5d1c71-1784-4060-92a6-3e5799757900","year":2021},"citing_paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T00:55:27.584622Z"},"links":{"citing_paper":"/paper/2506.12425"},"observation_digest":"sha256:94f3a7c764cc656a45d3418ed37d6a83591db81279267567fffc5b71139eb220","observation_id":"34a717c1-332a-40db-9094-a80918ffe5f6","resolution":{"observed_at":"2026-08-07T00:55:27.776944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.12425","last_updated":"2025-06-14T09:52:24Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-07T00:48:05.872623Z","submitted_at":"2025-06-14T09:52:24Z","title":"Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks"},"reference_resolution":{"displayed":15,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":14},"total_outbound_references":15},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.12425."}