Paper Citation Record · LEDGER

Learn Beneficial Noise as Graph Augmentation

As of 20 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 3 inbound Pith citation observations for arXiv:2505.19024.

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

pith.paper-citation-record.v1
2505.19024 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:28:35.805437Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:05:22.198820Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T12:43:44.319074Z

Reference resolution

19 of 19 outbound references displayed

verified exact2
verified fuzzy7
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cbf41ae4-b6d7-428a-83e5-f896a5c24a4f · outbound

This paper cites Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification.

Learn Beneficial Noise as Graph Augmentation Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:28:36.170469Z

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 3b8f338d-0570-4b59-a11e-6645d0627c93 · outbound

This paper cites Auto-Encoding Variational Bayes.

Learn Beneficial Noise as Graph Augmentation Auto-Encoding Variational Bayes

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:34.444667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 053f5683-97d1-45ef-9ab8-3c15ebeec71c · outbound

This paper cites We evaluate our proposed framework in the semi-supervised learning setting on graph classification on the benchmark TUDataset (Morris et al., 2020).

Learn Beneficial Noise as Graph Augmentation We evaluate our proposed framework in the semi-supervised learning setting on graph classification on the benchmark TUDataset (Morris et al., 2020)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:36.842769Z

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 581ed39f-7ad8-445c-8a80-f54b69911c2e · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

Learn Beneficial Noise as Graph Augmentation Geom-GCN: Geometric Graph Convolutional Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:34.696094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4ffcbea-3116-45cb-b5c1-3b150800b466 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

Learn Beneficial Noise as Graph Augmentation Dropout: a simple way to prevent neural networks from overfitting

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:37.582098Z

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 8b7543a3-f367-41e8-be4f-090235610d55 · outbound

This paper cites Specifically, we carry out grid search for the hyper-parameters on the following search space: • Number of training epochs: {500, 1000, 1500, 2000, 3000}.

Learn Beneficial Noise as Graph Augmentation Specifically, we carry out grid search for the hyper-parameters on the following search space: • Number of training epochs: {500, 1000, 1500, 2000, 3000}

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:36.514728Z

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 e0bb31d1-52b7-4c4d-94a6-8d5c91f8b766 · outbound

This paper cites Variational Positive-incentive Noise: How Noise Benefits Models.

Learn Beneficial Noise as Graph Augmentation Variational Positive-incentive Noise: How Noise Benefits Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:35.253981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc3da9a9-e450-4558-bb63-41b3d20dc033 · outbound

This paper cites Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise.

Learn Beneficial Noise as Graph Augmentation Data Augmentation of Contrastive Learning is Estimating Positive-incentive Noise

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:35.307034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a439eddb-5264-43e4-996c-d1f1cb202c89 · outbound

This paper cites Graph contrastive learning with adaptive augmentation.

Learn Beneficial Noise as Graph Augmentation Graph contrastive learning with adaptive augmentation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:37.374242Z

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 c0011200-7e2f-4b74-bb1b-3731577e3f59 · outbound

This paper cites The detailed statistics of the datasets are summarized in Table.

Learn Beneficial Noise as Graph Augmentation The detailed statistics of the datasets are summarized in Table

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:36.994253Z

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 1d6f47ef-0c1e-4238-b3c1-fb3de728f0a6 · outbound

This paper cites Dataset Graphs Avg.

Learn Beneficial Noise as Graph Augmentation Dataset Graphs Avg

Reference 256

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:36.648098Z

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 5701de91-013d-4370-b1b3-0a3a567404ad · outbound

This paper cites Representation Learning on Graphs: Methods and Applications.

Learn Beneficial Noise as Graph Augmentation Representation Learning on Graphs: Methods and Applications

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:34.040621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61372527-58fb-498b-b976-242eac5ab5d6 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Learn Beneficial Noise as Graph Augmentation How Powerful are Graph Neural Networks?

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:35.179306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc37ff59-d331-44de-9699-eed998b72aa4 · outbound

This paper cites A Framework For Contrastive Self-Supervised Learning And Designing A New Approach.

Learn Beneficial Noise as Graph Augmentation A Framework For Contrastive Self-Supervised Learning And Designing A New Approach

Reference 2020

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:28:36.328528Z

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 b3915efa-74c8-4c19-8544-90f98a886332 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Learn Beneficial Noise as Graph Augmentation Representation Learning with Contrastive Predictive Coding

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:35.063523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb1953ee-3147-471a-a21c-d47977c69a9e · outbound

This paper cites Large-Scale Representation Learning on Graphs via Bootstrapping.

Learn Beneficial Noise as Graph Augmentation Large-Scale Representation Learning on Graphs via Bootstrapping

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:34.915423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0821edb3-541e-46df-9aeb-4b09348b9dcf · outbound

This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

Learn Beneficial Noise as Graph Augmentation Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:34.567236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0961ecea-09f5-4226-a193-6c55f5421b14 · outbound

This paper cites Derivation of Eq.

Learn Beneficial Noise as Graph Augmentation Derivation of Eq

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:37.154731Z

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 b2d6806c-cba5-43ac-a16b-bbeff3916ff5 · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

Learn Beneficial Noise as Graph Augmentation Categorical Reparameterization with Gumbel-Softmax

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:34.293682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 18d9bb00-30da-49e3-882c-c46e90d7b08a · inbound

AnchorFormer: Differentiable Anchor Attention for Efficient Vision Transformer cites this paper.

AnchorFormer: Differentiable Anchor Attention for Efficient Vision Transformer Learn Beneficial Noise as Graph Augmentation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-07T15:05:22.198820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c780d8f5-a149-49de-9bfa-57b0541af426 · inbound

Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface cites this paper.

Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface Learn Beneficial Noise as Graph Augmentation

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T22:21:13.290940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fd88e7c-74d5-4b26-9af2-75bcd56416e7 · inbound

TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering cites this paper.

TableZoomer: A Collaborative Agent Framework for Large-scale Table Question Answering Learn Beneficial Noise as Graph Augmentation

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T12:43:44.412810Z

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.