Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:36:28.921250Z
Paper Citation Record · LEDGER
Adaptive Graph Unlearning
As of 18 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2505.12614.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
26 of 26 outbound references displayed
- verified exact1
- verified fuzzy21
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 83370cc8-97db-4b64-a79c-2be3d94a6dbc · outbound
This paper cites Graph unlearning.
Adaptive Graph Unlearning Graph unlearning
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.436217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 892652fc-e52d-4fc7-b041-f4f9cfc5e944 · outbound
This paper cites Efficient model updates for approximate un- learning of graph-structured data.
Adaptive Graph Unlearning Efficient model updates for approximate un- learning of graph-structured data
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.394025Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0dfbfae1-13e2-4109-afdc-76b5c3222630 · outbound
This paper cites Grapheditor: An efficient graph representation learn- ing and unlearning approach.
Adaptive Graph Unlearning Grapheditor: An efficient graph representation learn- ing and unlearning approach
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.379587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6e28f1ce-81dd-4c19-a117-4c531088bdc8 · outbound
This paper cites Graph neural networks.
Adaptive Graph Unlearning Graph neural networks
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.365907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ed7d0a8c-1d62-449d-8c1a-7a0c7e544b7c · outbound
This paper cites OpenGU: A Comprehensive Benchmark for Graph Unlearning.
Adaptive Graph Unlearning OpenGU: A Comprehensive Benchmark for Graph Unlearning
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T20:36:29.126172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d84eadef-47e9-4dd1-ac16-c9609c936504 · outbound
This paper cites Inductive representation learning on large graphs.
Adaptive Graph Unlearning Inductive representation learning on large graphs
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.324220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e4719909-001d-4e4e-b7be-fab1d36f0735 · outbound
This paper cites Semi-Supervised Classification with Graph Convolutional Networks.
Adaptive Graph Unlearning Semi-Supervised Classification with Graph Convolutional Networks
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:36:28.858819Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ed13d6f9-68d1-4ac6-b406-26a0109efea3 · outbound
This paper cites TCGU: Data-centric Graph Unlearning based on Transferable Condensation.
Adaptive Graph Unlearning TCGU: Data-centric Graph Unlearning based on Transferable Condensation
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:36:28.868081Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6680e604-2396-421e-bf38-656eb435902c · outbound
This paper cites Remem- ber what you want to forget: Algorithms for machine un- learning.
Adaptive Graph Unlearning Remem- ber what you want to forget: Algorithms for machine un- learning
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.295188Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3266725d-354c-4a55-b8ee-ebc2313dbaaa · outbound
This paper cites Graph attention networks.
Adaptive Graph Unlearning Graph attention networks
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.250136Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 19fedb7d-063d-4a72-a6b8-30d75b96fc37 · outbound
This paper cites Inductive graph unlearning.
Adaptive Graph Unlearning Inductive graph unlearning
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.234687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2acdb415-8f1f-48a4-800a-63a3ad357265 · outbound
This paper cites Sim- plifying graph convolutional networks.
Adaptive Graph Unlearning Sim- plifying graph convolutional networks
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.219869Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d64555a7-3415-49be-aaae-a2ccb146d886 · outbound
This paper cites How Powerful are Graph Neural Networks?.
Adaptive Graph Unlearning How Powerful are Graph Neural Networks?
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:36:28.898839Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3ed96317-6e45-4c6f-9028-709cf4712e17 · outbound
This paper cites Revisiting semi-supervised learning with graph embeddings.
Adaptive Graph Unlearning Revisiting semi-supervised learning with graph embeddings
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.203727Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 8ab1b391-9fdb-47f3-8d89-4f26965e3df2 · outbound
This paper cites Erase then rectify: A training- free parameter editing approach for cost-effective graph unlearning.
Adaptive Graph Unlearning Erase then rectify: A training- free parameter editing approach for cost-effective graph unlearning
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.187856Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2ab71058-fd68-434f-9a20-1f91355b5fea · outbound
This paper cites Scalable and cer- tifiable graph unlearning: Overcoming the approximation error barrier.
Adaptive Graph Unlearning Scalable and cer- tifiable graph unlearning: Overcoming the approximation error barrier
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.172685Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 82a9378b-e22b-49d0-8d36-6c9e12641f7e · outbound
This paper cites Graph- saint: Graph sampling based inductive learning method.
Adaptive Graph Unlearning Graph- saint: Graph sampling based inductive learning method
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.157247Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 60529417-0f62-459a-9a77-4f82ae8a78ad · outbound
This paper cites A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks.
Adaptive Graph Unlearning A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:36:28.863502Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d355d27-adc7-43e5-9ed9-70a3651b0a4f · outbound
This paper cites A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.Journal of Big Data, 11(1):18,.
Adaptive Graph Unlearning A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions.Journal of Big Data, 11(1):18,
Reference 2017
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.310051Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 97daa11d-a959-4471-b226-7e0ea3aa9a0e · outbound
This paper cites Unlink to unlearn: Simplifying edge unlearning in gnns.
Adaptive Graph Unlearning Unlink to unlearn: Simplifying edge unlearning in gnns
Reference 2018
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.264467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e5ef208a-893b-4c14-901d-22dfff9632a5 · outbound
This paper cites Be- yond homophily in graph neural networks: Current lim- itations and effective designs.
Adaptive Graph Unlearning Be- yond homophily in graph neural networks: Current lim- itations and effective designs
Reference 2019
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.141849Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 87cbdec7-2081-45b7-a63b-8fac666a2e7c · outbound
This paper cites Pitfalls of graph neural network evaluation.
Adaptive Graph Unlearning Pitfalls of graph neural network evaluation
Reference 2021
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.279393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e341a494-0e9b-46ab-8a38-dc1da1b6c66c · outbound
This paper cites Characterizing the influence of graph elements.
Adaptive Graph Unlearning Characterizing the influence of graph elements
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.422531Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3c818d6b-ed8a-47b5-b477-dd0145aa18e5 · outbound
This paper cites GNNDelete: A general strategy for unlearning in graph neural networks.
Adaptive Graph Unlearning GNNDelete: A general strategy for unlearning in graph neural networks
Reference 2023
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.408325Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 83aa5edb-084a-4665-a349-73515da041d0 · outbound
This paper cites AGU Appendix.
Adaptive Graph Unlearning AGU Appendix
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.352153Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 7fd374ed-99b0-46fa-94ce-cfb825eb4808 · outbound
This paper cites Idea: A flexible framework of certified unlearning for graph neural networks.
Adaptive Graph Unlearning Idea: A flexible framework of certified unlearning for graph neural networks
Reference 2025
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:36:29.337841Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.