Paper Citation Record · LEDGER

Towards Effective Graph Rationalization via Boosting Environment Diversity

As of 22 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2412.12880.

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

pith.paper-citation-record.v1
2412.12880 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:45:13.687679Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

46 of 46 outbound references displayed

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External citation measurements

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Outbound references

Observation 2ba0ce33-c572-4b05-bd14-29c1260e371b · outbound

This paper cites Pre-training molecular graph representation with 3d geometry,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Pre-training molecular graph representation with 3d geometry,

Reference 1

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Observation 2498e1a6-adef-4f7b-90d9-53ae0c73dbb0 · outbound

This paper cites Out-Of-Distribution Generalization on Graphs: A Survey.

Towards Effective Graph Rationalization via Boosting Environment Diversity Out-Of-Distribution Generalization on Graphs: A Survey

Reference 2

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Observation 12179cd1-cc45-4b45-87dc-127d8e010914 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Interpretable and generalizable graph learning via stochastic attention mechanism,

Reference 3

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Observation d26f11f8-8725-4a32-9697-024b7e1b1410 · outbound

This paper cites Individual and structural graph information bottlenecks for out- of-distribution generalization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Individual and structural graph information bottlenecks for out- of-distribution generalization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.443653Z

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Observation f54fed18-9573-4793-8224-b3dce2ee0697 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Graph invariant learning with subgraph co-mixup for out-of-distribution generalization,

Reference 5

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Observation 96920f7a-9c8e-443a-8e22-46aba2c5dd40 · outbound

This paper cites Learning causally invariant representations for out-of-distribution generalization on graphs,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Learning causally invariant representations for out-of-distribution generalization on graphs,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.410134Z

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Observation 796908df-c95f-4238-a6aa-7bb0365de8c0 · outbound

This paper cites Joint learning of label and environment causal independence for graph out-of-distribution general- ization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Joint learning of label and environment causal independence for graph out-of-distribution general- ization,

Reference 7

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Observation c8ebb507-d24a-4219-bc5c-331d42abff82 · outbound

This paper cites Causal attention for interpretable and generalizable graph classification,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Causal attention for interpretable and generalizable graph classification,

Reference 8

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Observation db6fd4c4-5d12-4c6f-bd9d-5f5f19b8321b · outbound

This paper cites Ood-gnn: Out-of-distribution generalized graph neural network,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Ood-gnn: Out-of-distribution generalized graph neural network,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.358444Z

Source-reported events for the cited work

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Observation 66f8a1a2-e0df-4a00-aaa9-8b51b5b088bb · outbound

This paper cites Learning causal representations for robust domain adaptation,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Learning causal representations for robust domain adaptation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.344111Z

Source-reported events for the cited work

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Observation 41d7b33d-c3b5-4cd5-a9d5-259a7a4e2fed · outbound

This paper cites Dive: Subgraph disagreement for graph out-of-distribution generalization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Dive: Subgraph disagreement for graph out-of-distribution generalization,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.329267Z

Source-reported events for the cited work

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Observation 373abfee-80bf-4374-8150-8694bc64e327 · outbound

This paper cites Discovering invariant rationales for graph neural networks,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Discovering invariant rationales for graph neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.314662Z

Source-reported events for the cited work

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Observation f4e25626-9b84-4af6-a9f0-717a4554a7d3 · outbound

This paper cites Graph rationalization with environment-based augmentations,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Graph rationalization with environment-based augmentations,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.295586Z

Source-reported events for the cited work

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Observation 6202c36d-1627-46b1-b438-29276d9f306b · outbound

This paper cites Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift.

Towards Effective Graph Rationalization via Boosting Environment Diversity Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

Reference 14

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Observation 9dff15d4-8d5f-4ce2-b5d1-9aa6a3bf4852 · outbound

This paper cites Mean shift: A robust approach toward feature space analysis,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Mean shift: A robust approach toward feature space analysis,

Reference 15

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Observation 34534859-bffd-4d2e-bf52-5d476a7245aa · outbound

This paper cites Gn- nexplainer: Generating explanations for graph neural networks,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Gn- nexplainer: Generating explanations for graph neural networks,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.263483Z

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Observation 542f924e-6d64-489c-8878-0d22255eaecc · outbound

This paper cites Does invariant graph learning via environment augmentation learn invariance?.

Towards Effective Graph Rationalization via Boosting Environment Diversity Does invariant graph learning via environment augmentation learn invariance?

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.246386Z

Source-reported events for the cited work

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Observation 91587cf7-7440-4cbf-b706-5d6a0d2a0378 · outbound

This paper cites Graph information bottleneck for subgraph recognition,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Graph information bottleneck for subgraph recognition,

Reference 18

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Observation 9ca4c1f1-517d-496a-b7a1-ccb242a9b595 · outbound

This paper cites Improving subgraph recognition with variational graph information bottleneck,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Improving subgraph recognition with variational graph information bottleneck,

Reference 19

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Observation 75d2c95d-1ba7-405f-b01b-11fe586debe5 · outbound

This paper cites Individual and structural graph information bottlenecks for out- of-distribution generalization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Individual and structural graph information bottlenecks for out- of-distribution generalization,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.184645Z

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Observation d18c7c79-a8aa-450e-a58e-82c979e28417 · outbound

This paper cites Invariant Risk Minimization.

Towards Effective Graph Rationalization via Boosting Environment Diversity Invariant Risk Minimization

Reference 21

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Observation 75ea5eba-1b6d-4cd7-bbd6-72297f4602d1 · outbound

This paper cites Invariance principle meets information bottleneck for out-of-distribution generalization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Invariance principle meets information bottleneck for out-of-distribution generalization,

Reference 22

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Observation f618ec0d-6a5e-4e22-a12e-7295892ef836 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Out-of-distribution generalization via risk extrapolation (rex),

Reference 23

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Observation c343c259-3d4e-4296-817b-058886d3c8d1 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Learning substructure invariance for out-of-distribution molecular representations,

Reference 24

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Observation 5ace0994-666f-4100-9193-c5629d796a40 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Learning invariant graph representations for out-of-distribution generalization,

Reference 25

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Observation 1b05ddb1-5c15-4685-8cc4-3d9f5b23e558 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Flood: A flexible invariant learning framework for out-of-distribution generalization on graphs,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.101564Z

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Observation 6bf7ed02-d12f-4f91-97eb-f91e310a321c · outbound

This paper cites Debiasing graph neural networks via learning disentangled causal substructure,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Debiasing graph neural networks via learning disentangled causal substructure,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.083813Z

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Observation 3edc0119-cbd8-406e-a517-8eb07c9f738e · outbound

This paper cites Advancing molecule invariant representation via privileged substructure identification,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Advancing molecule invariant representation via privileged substructure identification,

Reference 28

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Observation 214fb822-49aa-435b-aa82-e3892760d572 · outbound

This paper cites Learning from shortcut: A shortcut-guided approach for graph rationalization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Learning from shortcut: A shortcut-guided approach for graph rationalization,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.050826Z

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Observation 0684f97e-ca08-4351-bc23-b896ad5ec463 · outbound

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

Towards Effective Graph Rationalization via Boosting Environment Diversity Mind the label shift of augmentation-based graph ood generalization,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.035024Z

Source-reported events for the cited work

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Observation 7552269b-1892-43c3-b9e2-95fb1cbbc679 · outbound

This paper cites Cooperative classi- fication and rationalization for graph generalization,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Cooperative classi- fication and rationalization for graph generalization,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.018144Z

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Observation 84eaeaba-7e06-49ec-b3e8-fbb14b87c041 · outbound

This paper cites Categorical reparameterization with gumbel-softmax,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Categorical reparameterization with gumbel-softmax,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:14.001039Z

Source-reported events for the cited work

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Observation aaeafec0-d44d-4582-afef-b360de527d24 · outbound

This paper cites Boosting graph contrastive learning via graph contrastive saliency,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Boosting graph contrastive learning via graph contrastive saliency,

Reference 33

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Source-reported events for the cited work

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Observation fa835ea2-aa45-40d8-9a40-e5372be2edec · outbound

This paper cites Adversarial graph augmentation to improve graph contrastive learning,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Adversarial graph augmentation to improve graph contrastive learning,

Reference 34

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Observation 378ad4ee-26a7-4cf1-a912-7dc8077f3cda · outbound

This paper cites Data augmentation for graph neural networks,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Data augmentation for graph neural networks,

Reference 35

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Observation d81f7bbf-eb17-4d56-89bf-507a06caf78a · outbound

This paper cites Graph transplant: Node saliency-guided graph mixup with local structure preservation,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Graph transplant: Node saliency-guided graph mixup with local structure preservation,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.932344Z

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Observation 36fcbeb5-db9f-4f41-801d-30d7103f39cc · outbound

This paper cites Do generated data always help contrastive learning?.

Towards Effective Graph Rationalization via Boosting Environment Diversity Do generated data always help contrastive learning?

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.914589Z

Source-reported events for the cited work

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Observation 328cc99e-de60-4537-8449-30cf5508a87a · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Towards Effective Graph Rationalization via Boosting Environment Diversity A simple framework for contrastive learning of visual representations,

Reference 38

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Source-reported events for the cited work

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Observation 7b4ff83a-0588-4629-81fe-73a4c2df97ad · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Open graph benchmark: Datasets for machine learning on graphs,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.882169Z

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Observation c93fd6cb-70b6-4a45-96bf-3da673ba885a · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.865751Z

Source-reported events for the cited work

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Observation 3a2ae382-c594-44bc-8770-34519653520b · outbound

This paper cites Fast and accurate modeling of molecular atomization energies with machine learning,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Fast and accurate modeling of molecular atomization energies with machine learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.851155Z

Source-reported events for the cited work

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Observation a6addbf7-35d3-4e76-84bb-26ee731e1bd2 · outbound

This paper cites How powerful are graph neural networks?.

Towards Effective Graph Rationalization via Boosting Environment Diversity How powerful are graph neural networks?

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.831157Z

Source-reported events for the cited work

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Observation 8746ab2c-e6b0-446e-9bd9-011879a48577 · outbound

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Towards Effective Graph Rationalization via Boosting Environment Diversity Environment inference for invariant learning,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.815936Z

Source-reported events for the cited work

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Observation 06cd38ad-1c40-400d-8db2-2d0a67f1be05 · outbound

This paper cites Parameterized explainer for graph neural network,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Parameterized explainer for graph neural network,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.800812Z

Source-reported events for the cited work

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Observation fe6ec91d-f119-41ad-9de3-ce82d7ffe695 · outbound

This paper cites Interpreting graph neural networks for nlp with differentiable edge masking,.

Towards Effective Graph Rationalization via Boosting Environment Diversity Interpreting graph neural networks for nlp with differentiable edge masking,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.786335Z

Source-reported events for the cited work

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Observation be3ae9fb-e902-4148-9059-a488183b9937 · outbound

This paper cites The jensen-shannon divergence,.

Towards Effective Graph Rationalization via Boosting Environment Diversity The jensen-shannon divergence,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T13:45:13.771830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.