{"as_of":"2026-08-16T20:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6c65bbdd1fb7ed1cbb80e628c67f0aa57dde521a570774b8c4ad903a252d4e38","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:44:25.724725Z","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":"2026-05-11T12:51:02.718384Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.13953","last_updated":"2022-06-28T12:19:01Z","snapshot_observed_at":"2026-08-16T16:49:50.888457Z","submitted_at":"2022-06-28T12:19:01Z","title":"RAW-GNN: RAndom Walk Aggregation based Graph Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.13953","snapshot_observed_at":"2026-08-16T04:44:25.724725Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.00552","last_updated":"2025-05-01T14:28:44Z","snapshot_observed_at":"2026-08-16T04:37:17.941900Z","submitted_at":"2025-05-01T14:28:44Z","title":"Graph Spectral Filtering with Chebyshev Interpolation for Recommendation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:44:25.724725Z"},"links":{"cited_paper":"/paper/2206.13953","citing_paper":"/paper/2505.00552"},"observation_digest":"sha256:9926f826f6294eb4cd6f0a5ce9286fc07cdf4fac09a8651613c241fae172464b","observation_id":"a221ca93-86f4-44c1-9f50-fa4e6c793295","resolution":{"observed_at":"2026-08-16T04:44:25.724725Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.13953","last_updated":"2022-06-28T12:19:01Z","snapshot_observed_at":"2026-08-16T16:49:50.888457Z","submitted_at":"2022-06-28T12:19:01Z","title":"RAW-GNN: RAndom Walk Aggregation based Graph Neural Network","version":1},"cited_work":{"arxiv_id":"2206.13953","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.13953","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"4b1276a8-3446-479b-bfc4-a2eb7d6b5e88","year":2022},"citing_paper":{"arxiv_id":"2604.19186","last_updated":"2026-04-21T07:55:19Z","snapshot_observed_at":"2026-08-13T00:12:34.950898Z","submitted_at":"2026-04-21T07:55:19Z","title":"Inductive Subgraphs as Shortcuts: Causal Disentanglement for Heterophilic Graph Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T02:54:22.394142Z"},"links":{"cited_paper":"/paper/2206.13953","citing_paper":"/paper/2604.19186"},"observation_digest":"sha256:a3330f6daae0b12f81b0690f2e2a0e2b0aee22e364ba8f6419ef89a3a6ec34cf","observation_id":"94363b4e-fcee-4e28-8767-a85119949815","resolution":{"observed_at":"2026-05-11T12:51:02.721861Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2206.13953/citation-record","integrity":"/paper/2206.13953/integrity","json":"/paper/2206.13953/citation-record.json","paper":"/paper/2206.13953"},"outbound":[],"paper":{"arxiv_id":"2206.13953","last_updated":"2022-06-28T12:19:01Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T16:49:50.888457Z","submitted_at":"2022-06-28T12:19:01Z","title":"RAW-GNN: RAndom Walk Aggregation based Graph Neural Network"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2206.13953."}