Paper Citation Record · LEDGER

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias

As of 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2510.23469.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2510.23469 v2

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T07:55:01.971066Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

27 of 27 outbound references displayed

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verified fuzzy0
unresolved26
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7a93049-f24c-4643-a7a2-a089c38e128f · outbound

This paper cites This fine-grained gating mechanism is significantly more expressive than simple additive prompts or global transformations.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias This fine-grained gating mechanism is significantly more expressive than simple additive prompts or global transformations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.358290Z

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Observation b9198793-cf84-419b-a0f1-54cff50e088f · outbound

This paper cites The Adaptive Message Calibration (AMC) module directly intervenes in this process by injecting a layer-wise, edge-specific structure prompt e(l−1) ij into each message.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias The Adaptive Message Calibration (AMC) module directly intervenes in this process by injecting a layer-wise, edge-specific structure prompt e(l−1) ij into each message

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.364741Z

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Observation fb24d40d-795e-46d1-8552-683fff6b1395 · outbound

This paper cites Learning discrete structures for graph neural networks.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Learning discrete structures for graph neural networks

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.088110Z

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Observation 7e0250bc-4e41-4f43-b8bc-ceabe0021787 · outbound

This paper cites AMC serves as a layer-wise regularizer by generating edge-specific calibration vectors according to equation.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias AMC serves as a layer-wise regularizer by generating edge-specific calibration vectors according to equation

Reference 4

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Observation 03a8230a-e258-411d-859a-b2211a8489de · outbound

This paper cites Variational Graph Auto-Encoders.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Variational Graph Auto-Encoders

Reference 5

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Observation 177dcd4b-c880-4560-950d-75850fa78b65 · outbound

This paper cites an unresolved cited work.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.744741Z

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Observation c4a55172-468f-4a4d-8092-b32a75a565b9 · outbound

This paper cites Highway Networks.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Highway Networks

Reference 11

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Observation ce9aaa85-6780-47ef-b72c-e56ad96f36bd · outbound

This paper cites Bootstrapped representation learning on graphs.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Bootstrapped representation learning on graphs

Reference 13

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Observation c7cd3728-7fc0-4725-9505-0478a64a7e56 · outbound

This paper cites The information bottleneck method.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias The information bottleneck method

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:00.792862Z

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Observation e1477338-4ce6-4356-bb85-2f8019800dec · outbound

This paper cites Improving Fairness in Graph Neural Networks via Counterfactual Debiasing.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Improving Fairness in Graph Neural Networks via Counterfactual Debiasing

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.106487Z

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Observation c450907f-ac58-4729-a405-fe2e76c9d10a · outbound

This paper cites How Powerful are Graph Neural Networks?.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias How Powerful are Graph Neural Networks?

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.172023Z

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Observation d8a73c8f-2169-49cc-acde-66c0e45162e4 · outbound

This paper cites Deep Graph Contrastive Representation Learning.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Deep Graph Contrastive Representation Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.225653Z

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Observation 4d445a9b-f64c-4cb0-8672-2443cdcff104 · outbound

This paper cites We demonstrate that ADPrompt can effectively adapt a fixed pre-trained GNNθ∗ to diverse downstream tasks through the learned adaptive dual prompts.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias We demonstrate that ADPrompt can effectively adapt a fixed pre-trained GNNθ∗ to diverse downstream tasks through the learned adaptive dual prompts

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.297657Z

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Observation 832f9942-a542-491e-a6b1-d43967d498a9 · outbound

This paper cites Raw node attributesXoften contain sen- sitive information correlated withS, forming the initial source of bias.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Raw node attributesXoften contain sen- sitive information correlated withS, forming the initial source of bias

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.453953Z

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Unavailable: canonical work link unavailable.

Observation ac55b707-fb20-42c4-97e6-e3cc83722b89 · outbound

This paper cites an unresolved cited work.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:01.552113Z

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Observation 9e9c6f38-1f88-4361-8855-74027eaf3787 · outbound

This paper cites working field.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias working field

Reference 26

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Observation 2cffe417-0ce9-4027-9cf8-7464c3471e80 · outbound

This paper cites It incorporates a fixed prompt to represent sensitive group em- beddings and a learnable prompt to bridge the gap between pre-training and downstream tasks.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias It incorporates a fixed prompt to represent sensitive group em- beddings and a learnable prompt to bridge the gap between pre-training and downstream tasks

Reference 27

Resolution: malformed identifier; no resolver link, observed 2026-08-04T07:55:01.971066Z

Source-reported events for the cited work

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Observation 617be5d3-3265-4e7a-95d0-91cd21052773 · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:00.157299Z

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Observation 53d27dc8-828a-4082-bb7c-f52eb03c2801 · outbound

This paper cites Deep Graph Infomax.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Deep Graph Infomax

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:00.965539Z

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Observation 8355b832-a498-4ca9-9c3e-e1b83cf38ab0 · outbound

This paper cites Yutai Duan, Jie Liu, Shaowei Chen, Liyi Chen, and Jianhua Wu.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Yutai Duan, Jie Liu, Shaowei Chen, Liyi Chen, and Jianhua Wu

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:58.935016Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0422122-4f74-4997-bcd0-c8a7158878da · outbound

This paper cites Graph Neural Networks Exponentially Lose Expressive Power for Node Classification.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Graph Neural Networks Exponentially Lose Expressive Power for Node Classification

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.999444Z

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Observation 1f809315-5305-4924-adb7-14b6de990dd1 · outbound

This paper cites Auc-oriented graph neural network for fraud detection.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Auc-oriented graph neural network for fraud detection

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.266166Z

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Unavailable: canonical work link unavailable.

Observation 04c6b3ee-1f90-412e-8d11-30ce9783a156 · outbound

This paper cites Fairness-aware prompt tuning for graph neural networks.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Fairness-aware prompt tuning for graph neural networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.675313Z

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Unavailable: canonical work link unavailable.

Observation d3b00fe2-c482-4d0f-aafc-994d2a015f66 · outbound

This paper cites Graph Prompt Learning: A Comprehensive Survey and Beyond.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Graph Prompt Learning: A Comprehensive Survey and Beyond

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T07:55:00.479053Z

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Observation 29ed07e8-0f78-48da-9e81-2aa7d2d420fb · outbound

This paper cites Edits: Modeling and mitigating data bias for graph neural networks.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Edits: Modeling and mitigating data bias for graph neural networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:58.772445Z

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Observation 642e6daa-82cb-4816-84cc-771fb1a20cf0 · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.553942Z

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Observation 927819af-ea76-43bf-8489-fdccda292691 · outbound

This paper cites Single Event Effects Assessment of UltraScale+ MPSoC Systems under Atmospheric Radiation.

Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias Single Event Effects Assessment of UltraScale+ MPSoC Systems under Atmospheric Radiation

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-04T07:54:59.762293Z

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Pith citing papers

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