Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.16374.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T18:38:27.621171Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T09:07:39.207642Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 50fe23b2-9393-4f30-bf3b-d7e7e4130404 · inbound
Robust OOD Graph Learning via Mean Constraints and Noise Reduction cites this paper.
Robust OOD Graph Learning via Mean Constraints and Noise Reduction Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:38:27.621171Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a39a1f49-4af1-4ea3-a4c8-93b9b961cf5f · inbound
$\text{M}^{2}$LLM: Multi-view Molecular Representation Learning with Large Language Models cites this paper.
$\text{M}^{2}$LLM: Multi-view Molecular Representation Learning with Large Language Models Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Reference 2018
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:39:23.240844Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e2d8f5f4-ffa3-40dc-9c54-ca4936cd9cbc · inbound
DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation cites this paper.
DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T09:07:39.210364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bf01bb19-72ac-45fc-8b5c-8809f8f7ab93 · inbound
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation cites this paper.
DSBD: Dual-Aligned Structural Basis Distillation for Graph Domain Adaptation Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T19:38:10.557951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3da1b5a7-1017-47e0-8819-d77309fbcd2a · inbound
Cross-Resolution Semantic Learning for Graph Domain Adaptation cites this paper.
Cross-Resolution Semantic Learning for Graph Domain Adaptation Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T08:31:17.708203Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.