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.

pith.paper-citation-record.v1
2505.12614 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:36:28.921250Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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
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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

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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

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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

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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

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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

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Source-reported events for the cited work

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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

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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

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Source-reported events for the cited work

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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

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Source-reported events for the cited work

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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

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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

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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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

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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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

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Source-reported events for the cited work

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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 2026-08-17T06:30:58.91139+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

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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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.