Paper Citation Record · LEDGER

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization

As of 15 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 1 inbound Pith citation observation for arXiv:2506.05957.

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

pith.paper-citation-record.v1
2506.05957 v4

Coverage vector

measured 62 of 62 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:19:32.445418Z

measured 63 of 63 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T21:30:43.925179Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T21:31:39.372965Z

Reference resolution

62 of 62 outbound references displayed

verified exact0
verified fuzzy34
unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ae9158d-ce6e-4276-9055-e77f6cbbf969 · outbound

This paper cites Invariance principle meets information bottleneck for out-of-distribution generalization.Advances in Neural Information Processing Systems, 34:3438–3450, 2021.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Invariance principle meets information bottleneck for out-of-distribution generalization.Advances in Neural Information Processing Systems, 34:3438–3450, 2021

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:27.759891Z

Source-reported events for the cited work

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Observation 9bd8c6fa-d268-4d62-a675-9b2ca028cb57 · outbound

This paper cites Invariant Risk Minimization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Invariant Risk Minimization

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:27.851480Z

Source-reported events for the cited work

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Observation a1cee937-82be-417b-9445-c9cd645e3400 · outbound

This paper cites The properties of known drugs.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization The properties of known drugs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:27.914560Z

Source-reported events for the cited work

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Observation b9b4e8fb-d43d-4d9f-a0e5-c4fdb2674f1c · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:28.008937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa5104c2-2f38-4f2e-80ec-767ec4262b6a · outbound

This paper cites Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks.Advances in Neural Information Processing Systems, 35:31871–31885, 2022.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Sizeshiftreg: a regularization method for improving size-generalization in graph neural networks.Advances in Neural Information Processing Systems, 35:31871–31885, 2022

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:38.462399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c8fef682-f5b3-45c9-9a4c-702c72eae67f · outbound

This paper cites Does invariant graph learning via environment augmentation learn invariance? InThirty-seventh Conference on Neural Information Processing Systems, 2023.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Does invariant graph learning via environment augmentation learn invariance? InThirty-seventh Conference on Neural Information Processing Systems, 2023

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:38.168950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9ba89914-727c-4941-be1c-2a334aceba78 · outbound

This paper cites Understanding and improving feature learning for out-of-distribution generalization.Advances in Neural Information Processing Systems, 36:68221–68275, 2023.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Understanding and improving feature learning for out-of-distribution generalization.Advances in Neural Information Processing Systems, 36:68221–68275, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.913040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2254271b-2603-4f66-a94a-02ff77359a8b · outbound

This paper cites Learning causally invariant representations for out-of-distribution generalization on graphs.Advances in Neural Information Processing Systems, 35:22131–22148, 2022.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Learning causally invariant representations for out-of-distribution generalization on graphs.Advances in Neural Information Processing Systems, 35:22131–22148, 2022

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.784379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 12dd7b86-316f-463e-8143-b5de3861b8be · outbound

This paper cites Environment inference for invariant learning.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Environment inference for invariant learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.657608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8e014ad0-9bf6-4a93-b955-0c98f0553606 · outbound

This paper cites Debiasing graph neural networks via learning disentangled causal substructure.Advances in Neural Information Processing Systems, 35:24934–24946, 2022.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Debiasing graph neural networks via learning disentangled causal substructure.Advances in Neural Information Processing Systems, 35:24934–24946, 2022

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.561067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 881c8a07-d246-4f6c-8de0-47cfdcf80bed · outbound

This paper cites Fast graph representation learning with pytorch geometric, 2019.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Fast graph representation learning with pytorch geometric, 2019

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:28.623668Z

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Observation 8ea08b2d-c825-47f7-877f-edbcae84c7a4 · outbound

This paper cites Neural message passing for quantum chemistry.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Neural message passing for quantum chemistry

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:28.683836Z

Source-reported events for the cited work

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Observation 699eb243-fae1-4b49-9c18-6a08d50caf2e · outbound

This paper cites GOOD: A graph out-of-distribution benchmark.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization GOOD: A graph out-of-distribution benchmark

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:28.722463Z

Source-reported events for the cited work

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Observation e0c05fe9-5c56-455f-97f5-0fc852e11505 · outbound

This paper cites Joint learning of label and environ- ment causal independence for graph out-of-distribution generalization.Advances in Neural Information Processing Systems, 36, 2023.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Joint learning of label and environ- ment causal independence for graph out-of-distribution generalization.Advances in Neural Information Processing Systems, 36, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.430931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 93c316b6-0348-4d7d-a949-270ad3e6698e · outbound

This paper cites G-mixup: Graph data augmentation for graph classification.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization G-mixup: Graph data augmentation for graph classification

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.284409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d8adb953-8291-402b-9f71-3cda6f65dcda · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.Advances in Neural Information Processing Systems, 33:22118–22133, 2020.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Open graph benchmark: Datasets for machine learning on graphs.Advances in Neural Information Processing Systems, 33:22118–22133, 2020

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.163248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 67918fd9-1a69-44f6-b039-e8e94cd3d0c4 · outbound

This paper cites Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:28.929456Z

Source-reported events for the cited work

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Observation a15916b6-7b16-4685-9e01-1a450bc3c0b4 · outbound

This paper cites DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:28.981312Z

Source-reported events for the cited work

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Observation ad561732-8d08-47b6-a849-716bbd805913 · outbound

This paper cites Graph invariant learning with subgraph co-mixup for out-of-distribution generalization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Graph invariant learning with subgraph co-mixup for out-of-distribution generalization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:37.026437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6956a8ed-a1dc-4743-b1d8-5af31cad433b · outbound

This paper cites Enforcing Predictive Invariance across Structured Biomedical Domains.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Enforcing Predictive Invariance across Structured Biomedical Domains

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T10:19:32.774912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5fe26058-8922-450a-a25a-178532b57bd2 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Adam: A Method for Stochastic Optimization

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:29.191462Z

Source-reported events for the cited work

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Observation e52a109b-f9fa-4c67-89fa-ef66a6453ac4 · outbound

This paper cites Kipf and Max Welling.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Kipf and Max Welling

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:29.257499Z

Source-reported events for the cited work

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Observation 317b3238-6065-4901-a553-e9baa5d78a04 · outbound

This paper cites Last layer re-training is sufficient for robustness to spurious correlations.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Last layer re-training is sufficient for robustness to spurious correlations

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.881133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fd67809-96a7-4bb2-8582-3ab0b1244d54 · outbound

This paper cites Wilds: A benchmark of in- the-wild distribution shifts.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Wilds: A benchmark of in- the-wild distribution shifts

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.762317Z

Source-reported events for the cited work

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Observation 79482327-acbb-40e0-bc30-1b3c22923fd8 · outbound

This paper cites Robust optimization as data augmentation for large-scale graphs.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Robust optimization as data augmentation for large-scale graphs

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.643597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0d5cffb-6a0d-44b2-8d04-46accd4b13f2 · outbound

This paper cites Out-of-distribution generalization via risk extrapolation (rex).

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Out-of-distribution generalization via risk extrapolation (rex)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.533447Z

Source-reported events for the cited work

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Observation 296b8a57-ed0f-48dd-b745-a4f4b17a84dd · outbound

This paper cites A reduction of a graph to a canonical form and an algebra arising during this reduction.Nauchno-Technicheskaya Informatsiya, 2(9):12–16, 1968.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization A reduction of a graph to a canonical form and an algebra arising during this reduction.Nauchno-Technicheskaya Informatsiya, 2(9):12–16, 1968

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:29.647076Z

Source-reported events for the cited work

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Observation 7c5a92a5-cfdf-4495-9196-e661db409fdc · outbound

This paper cites Ood-gnn: Out-of-distribution generalized graph neural network.IEEE Transactions on Knowledge and Data Engineering, 2022.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Ood-gnn: Out-of-distribution generalized graph neural network.IEEE Transactions on Knowledge and Data Engineering, 2022

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.423610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7c8a9c06-fa01-4678-a377-2f6993976976 · outbound

This paper cites Learning invariant graph representations for out-of-distribution generalization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Learning invariant graph representations for out-of-distribution generalization

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.311279Z

Source-reported events for the cited work

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Observation 9fb5ceaa-a0d7-4251-9648-97b184c10fe0 · outbound

This paper cites Invariant node representation learning under distribution shifts with multiple latent environments.ACM Transactions on Information Systems, 42(1):1– 30, 2023.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Invariant node representation learning under distribution shifts with multiple latent environments.ACM Transactions on Information Systems, 42(1):1– 30, 2023

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.173546Z

Source-reported events for the cited work

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Observation 075a72d6-07bf-4dbf-82e2-94d442263d76 · outbound

This paper cites Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Graph Structure and Feature Extrapolation for Out-of-Distribution Generalization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:29.813850Z

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Observation 331e7216-cdde-4d2f-9e27-ede23c27dd95 · outbound

This paper cites Graph structure extrapolation for out-of-distribution generalization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Graph structure extrapolation for out-of-distribution generalization

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:36.039442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9c2264b9-f546-4de5-a4da-de8fdb2e9acd · outbound

This paper cites Graph rationalization with environment- based augmentations.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Graph rationalization with environment- based augmentations

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:35.882865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 833c2327-28e6-4c0d-94f9-193aeca1a3e8 · outbound

This paper cites Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:35.690693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1ce8be4e-df53-4951-9e72-60d1bb262184 · outbound

This paper cites Parameterized explainer for graph neural network.Advances in Neural Information Processing Systems, 33:19620–19631, 2020.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Parameterized explainer for graph neural network.Advances in Neural Information Processing Systems, 33:19620–19631, 2020

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.017605Z

Source-reported events for the cited work

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Observation 6d44db18-9435-473c-85a6-ad5cf82b5936 · outbound

This paper cites The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.067147Z

Source-reported events for the cited work

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Observation 42f4d0be-e37c-4642-96af-117b63a1e6ff · outbound

This paper cites Interpretable and generalizable graph learning via stochastic attention mechanism.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Interpretable and generalizable graph learning via stochastic attention mechanism

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:35.386309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8e7bdb8f-1e94-4fa2-858f-39908b72cf4b · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library, 2019.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Pytorch: An imperative style, high-performance deep learning library, 2019

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.246351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e85196b3-6fff-4a49-857e-ef495d36cfc3 · outbound

This paper cites an unresolved cited work.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Unresolved cited work

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.338741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0a76a1a-3aeb-421c-8615-1d7364738f20 · outbound

This paper cites Gradient starvation: A learning proclivity in neural networks.Advances in Neural Information Processing Systems, 34:1256–1272, 2021.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Gradient starvation: A learning proclivity in neural networks.Advances in Neural Information Processing Systems, 34:1256–1272, 2021

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.411468Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1796d800-1931-4a85-a937-51f2bf23a051 · outbound

This paper cites On the spectral bias of neural networks: International conference on machine learning.arXiv, 2019.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization On the spectral bias of neural networks: International conference on machine learning.arXiv, 2019

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:35.142294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4a7bce00-ecb2-4c27-baa4-dee6bb36769c · outbound

This paper cites DropEdge: Towards Deep Graph Convolutional Networks on Node Classification.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.640457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b93b6ecd-adfa-4f71-b1bc-96b2ab37227c · outbound

This paper cites Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.793663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf28ca0c-d013-41d3-8018-f3eda54a3d74 · outbound

This paper cites The pitfalls of simplicity bias in neural networks.Advances in Neural Information Processing Systems, 33:9573–9585, 2020.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization The pitfalls of simplicity bias in neural networks.Advances in Neural Information Processing Systems, 33:9573–9585, 2020

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:30.891178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c60ba7ff-cb0f-4930-874a-093ade83221c · outbound

This paper cites Manning, Andrew Ng, and Christopher Potts.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Manning, Andrew Ng, and Christopher Potts

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.980793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ccf20227-4109-487a-8c1b-398c6e4eae78 · outbound

This paper cites Unleashing the power of graph data augmentation on covariate distribution shift.Advances in Neural Information Processing Systems, 36, 2023.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Unleashing the power of graph data augmentation on covariate distribution shift.Advances in Neural Information Processing Systems, 36, 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.827139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation acfef263-7fac-4f47-915f-e9f9a6919428 · outbound

This paper cites Deep learning and the information bottleneck principle, 2015.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Deep learning and the information bottleneck principle, 2015

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.726580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe09550f-b12e-48db-8dd0-df2760c31307 · outbound

This paper cites Vapnik.The nature of statistical learning theory.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Vapnik.The nature of statistical learning theory

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.526006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4e8da241-3c1e-4ff6-a017-d047f9e47e9f · outbound

This paper cites Graph Attention Networks.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Graph Attention Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:31.247964Z

Source-reported events for the cited work

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Observation d0fb5c9f-176a-4108-b4c6-f3ee081000e0 · outbound

This paper cites Advancing molecule invariant represen- tation via privileged substructure identification.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Advancing molecule invariant represen- tation via privileged substructure identification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.416430Z

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Observation 9bcfc322-dbe5-4d9b-8ea9-5616e0879d97 · outbound

This paper cites Handling Distribution Shifts on Graphs: An Invariance Perspective.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Handling Distribution Shifts on Graphs: An Invariance Perspective

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:31.465793Z

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Observation 39ec979b-77c2-4a9e-8191-611191eeb57b · outbound

This paper cites Discovering invariant rationales for graph neural networks.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Discovering invariant rationales for graph neural networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.253383Z

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Observation ed685275-35b8-43a9-b68a-e35f94aa0b92 · outbound

This paper cites Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2):513–530, 2018.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Moleculenet: a benchmark for molecular machine learning.Chemical science, 9(2):513–530, 2018

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:31.649571Z

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Observation df7776f3-010f-4723-b998-793b0b26edc5 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization How Powerful are Graph Neural Networks?

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:31.724411Z

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Observation 569d6915-1afd-4151-a59d-66b322f06583 · outbound

This paper cites Learning substructure invariance for out-of-distribution molecular representations.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Learning substructure invariance for out-of-distribution molecular representations

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:34.047738Z

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Observation 3e4bd95a-017a-483a-917e-92200d80ba98 · outbound

This paper cites Improving out- of-distribution robustness via selective augmentation.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Improving out- of-distribution robustness via selective augmentation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:33.849734Z

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Observation 7863c61b-bf9f-4c90-9d98-7c154a9918a6 · outbound

This paper cites Empowering graph invariance learning with deep spurious infomax.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Empowering graph invariance learning with deep spurious infomax

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:33.659443Z

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Observation 4343ce90-9017-4186-85e0-f39f89f8811e · outbound

This paper cites Learning graph invariance by harnessing spuriosity.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Learning graph invariance by harnessing spuriosity

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:33.507188Z

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Observation a47c2eb2-d1da-4535-96c3-9a027bc3a607 · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.Advances in Neural Information Processing Systems, 32, 2019.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Gnnexplainer: Generating explanations for graph neural networks.Advances in Neural Information Processing Systems, 32, 2019

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:32.170857Z

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Observation f85fdc5a-9a53-42ec-9bde-db5946f954e4 · outbound

This paper cites Mind the label shift of augmentation-based graph ood generalization.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Mind the label shift of augmentation-based graph ood generalization

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:33.362524Z

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Observation bf9e3cd6-c425-4adc-aad6-a82705162323 · outbound

This paper cites Explainability in graph neural networks: A taxonomic survey.IEEE transactions on pattern analysis and machine intelligence, 45(5):5782–5799, 2022.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Explainability in graph neural networks: A taxonomic survey.IEEE transactions on pattern analysis and machine intelligence, 45(5):5782–5799, 2022

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:33.231185Z

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Observation abb3e4c5-41dc-48d2-a44c-7d35d6901231 · outbound

This paper cites Learning invariant molecular representation in latent discrete space.Advances in Neural Information Processing Systems, 36, 2023.

Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization Learning invariant molecular representation in latent discrete space.Advances in Neural Information Processing Systems, 36, 2023

Reference 62

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T10:19:33.043598Z

Source-reported events for the cited work

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Pith citing papers

Observation 025759ac-95c9-473f-8a1f-91fc955e1981 · inbound

AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation cites this paper.

AdvSynGNN: Structure-Adaptive Graph Neural Nets via Adversarial Synthesis and Self-Corrective Propagation Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T21:31:39.375441Z

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