Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization | Paper Citation Record

Observation 12468a6a-869c-4260-af65-c4b5251ecb83 · outbound

This paper cites Arnetminer: extraction and mining of academic social networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Arnetminer: extraction and mining of academic social networks,

Reference 1

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Observation 44087d52-179b-47a6-a7b4-a6ac6b999776 · outbound

This paper cites Inferring networks of substi- tutable and complementary products,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Inferring networks of substi- tutable and complementary products,

Reference 2

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Observation cf7e3457-f7be-44d4-90e9-5a53709380da · outbound

This paper cites Meta- gnn: On few-shot node classification in graph meta-learning,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Meta- gnn: On few-shot node classification in graph meta-learning,

Reference 3

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This paper cites Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking,

Reference 4

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Observation b81541d2-f64d-4462-b9a4-e48baeae5be1 · outbound

This paper cites Graph few-shot class-incremental learning,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graph few-shot class-incremental learning,

Reference 5

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Observation 54f6a890-fd67-4006-99bf-7fc0829964a6 · outbound

This paper cites Contrastive meta-learning for few- shot node classification,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Contrastive meta-learning for few- shot node classification,

Reference 6

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Observation 302c90f6-a4ba-4e95-b681-9946e26e8fa3 · outbound

This paper cites Semi-supervised classification with graph convolutional networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Semi-supervised classification with graph convolutional networks,

Reference 7

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This paper cites Graph attention networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graph attention networks,

Reference 8

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Observation 30e5ec93-5317-4a59-9387-a71ef0918e5a · outbound

This paper cites Graph neural networks: A review of methods and applications,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graph neural networks: A review of methods and applications,

Reference 9

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This paper cites Heterogeneous network embedding via deep architectures,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Heterogeneous network embedding via deep architectures,

Reference 10

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Observation d49a10f0-03b6-4507-98ea-4fbe7c88863b · outbound

This paper cites Inductive representation learning on large graphs,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Inductive representation learning on large graphs,

Reference 11

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Observation 81d3cc92-e2cb-41f5-82c3-9a8ea1d4c795 · outbound

This paper cites How powerful are graph neural networks?.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization How powerful are graph neural networks?

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:04.018436Z

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Observation a100ac12-14e4-4cb1-beb8-b5d8d0ff24db · outbound

This paper cites Graph few-shot learning with task-specific structures,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graph few-shot learning with task-specific structures,

Reference 13

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Observation f2b9ea0d-4a6c-41f8-af96-bd1a2d9213f6 · outbound

This paper cites Deep neural networks for learning graph representations,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Deep neural networks for learning graph representations,

Reference 14

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Interpretable and generalizable graph learning via stochastic attention mechanism,

Reference 15

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Observation 9c3331fa-6900-4761-8bdc-7c55ca059090 · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Mind the label shift of augmentation-based graph ood generalization,

Reference 16

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Observation 7f21e6ec-39fe-4c96-9c75-6080b8bedfc4 · outbound

This paper cites Safety in Graph Machine Learning: Threats and Safeguards.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Safety in Graph Machine Learning: Threats and Safeguards

Reference 17

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This paper cites Handling distribution shifts on graphs: An invariance perspective,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Handling distribution shifts on graphs: An invariance perspective,

Reference 18

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Observation bd57bd47-a4f3-4b31-b3c9-bd6a46f0b5b8 · outbound

This paper cites Graphrnn: Generating realistic graphs with deep auto-regressive models,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graphrnn: Generating realistic graphs with deep auto-regressive models,

Reference 19

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Observation 065f81f9-dd1b-42e8-8b44-3ce18fa9c9d1 · outbound

This paper cites Graph neural networks with convolutional arma filters,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graph neural networks with convolutional arma filters,

Reference 20

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Observation 4f189ba8-bc1c-450f-8098-d09045027e88 · outbound

This paper cites Domain adaptation: Learning bounds and algorithms,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Domain adaptation: Learning bounds and algorithms,

Reference 21

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This paper cites Generalizing from several related classification tasks to a new unlabeled sample,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Generalizing from several related classification tasks to a new unlabeled sample,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.929016Z

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This paper cites Domain generalization via invariant feature representation,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Domain generalization via invariant feature representation,

Reference 23

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This paper cites Recognition in terra incognita,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Recognition in terra incognita,

Reference 24

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This paper cites Do imagenet classifiers generalize to imagenet?.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Do imagenet classifiers generalize to imagenet?

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.897416Z

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This paper cites One pixel attack for fooling deep neural networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization One pixel attack for fooling deep neural networks,

Reference 26

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Observation a4b700c4-2f16-43a8-bfb9-2f2c2943c765 · outbound

This paper cites Collective spammer detection in evolving multi-relational social networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Collective spammer detection in evolving multi-relational social networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.879298Z

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Observation 3f987b30-b16b-4806-8a82-a44e362a7af4 · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Good: A graph out-of-distribution benchmark,

Reference 28

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Observation 45e2db41-ac8e-433b-bdc2-d5bf5e86199e · outbound

This paper cites Few-shot node classification with extremely weak supervision,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Few-shot node classification with extremely weak supervision,

Reference 29

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Observation c9f531b8-d383-4048-85b2-4df5fe9cd111 · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Out-of-distribution generalization via risk extrapolation (rex),

Reference 30

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This paper cites Invariant rationalization,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Invariant rationalization,

Reference 31

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This paper cites Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing,

Reference 32

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This paper cites Dark model adaptation: Semantic image segmentation from daytime to nighttime,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Dark model adaptation: Semantic image segmentation from daytime to nighttime,

Reference 33

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Discovering invariant rationales for graph neural networks,

Reference 34

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This paper cites Invariant risk minimization,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Invariant risk minimization,

Reference 35

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This paper cites Unsupervised domain adaptation by backpropagation,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Unsupervised domain adaptation by backpropagation,

Reference 36

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This paper cites Domain generalization with adversarial feature learning,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Domain generalization with adversarial feature learning,

Reference 37

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Observation 5d313ff3-6e75-472e-9ed1-cfbe10c29909 · outbound

This paper cites Graph prototypical networks for few-shot learning on attributed networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Graph prototypical networks for few-shot learning on attributed networks,

Reference 38

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This paper cites Return of frustratingly easy domain adaptation,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Return of frustratingly easy domain adaptation,

Reference 39

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Observation f6ffcef4-5352-4d68-8d42-c4e0f2823f96 · outbound

This paper cites Invariance, causality and robustness,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Invariance, causality and robustness,

Reference 40

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This paper cites Invariant causal prediction for nonlinear models,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Invariant causal prediction for nonlinear models,

Reference 41

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Observation 13f16d0c-0ee8-4cdf-a086-1d6e5a1e3e05 · outbound

This paper cites Does distributionally robust supervised learning give robust classifiers?.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Does distributionally robust supervised learning give robust classifiers?

Reference 42

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Observation 73f7cb80-1e57-41da-9dff-ecb33b170df1 · outbound

This paper cites Robust optimization over multiple domains,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Robust optimization over multiple domains,

Reference 43

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This paper cites Distributionally robust neural networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Distributionally robust neural networks,

Reference 44

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Observation 589d58e7-a91c-4e07-9ecd-d16244519242 · outbound

This paper cites Adversarial weight perturbation improves generalization in graph neural networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Adversarial weight perturbation improves generalization in graph neural networks,

Reference 45

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Observation 653b3694-ffdf-4d65-af43-988a023c3dfa · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

Reference 46

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Observation 36ec7b7c-8932-4fd8-9cf8-999045cbf60f · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Learning invariant graph representations for out-of-distribution generalization,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.699773Z

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Observation c672bfbf-161e-4356-bb7f-07cef1810158 · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Learning causally invariant representations for out-of- distribution generalization on graphs,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.690351Z

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Observation 8c60d2af-6141-4453-b52b-23c656961990 · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Debiasing graph neural networks via learning disentangled causal substructure,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.680615Z

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Observation 4410f838-7e8f-4ec1-9ab0-d9b65d469261 · outbound

This paper cites Does in- variant graph learning via environment augmentation learn invariance?.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Does in- variant graph learning via environment augmentation learn invariance?

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.670619Z

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Observation 524b4a31-6da5-4b62-a459-2bc25700ea6e · outbound

This paper cites Categorical reparameterization with gumbel-softmax,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Categorical reparameterization with gumbel-softmax,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.660209Z

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Observation 8e974dd4-f788-47d8-be76-88f4d1a634d1 · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Causal attention for interpretable and generalizable graph classification,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.649072Z

Source-reported events for the cited work

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Observation b4b8b0cc-391b-4a18-bec6-45964444fed5 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Representation Learning with Contrastive Predictive Coding

Reference 53

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Observation a675727f-2b30-4db6-84b8-d942a37157f4 · outbound

This paper cites Parameterized explainer for graph neural network,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Parameterized explainer for graph neural network,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.637268Z

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Observation c2218606-fefd-45d2-9c09-250befc49267 · 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.

Enhancing Distribution and Label Consistency 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 55

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Observation 30a842a5-f3d5-4185-b5bb-233242007b05 · outbound

This paper cites Gnnex- plainer: Generating explanations for graph neural networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Gnnex- plainer: Generating explanations for graph neural networks,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.626378Z

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Observation 62f10fad-aab8-4c56-957e-1f5ee2fc2777 · outbound

This paper cites Understanding attention and generalization in graph neural networks,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Understanding attention and generalization in graph neural networks,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.615274Z

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Observation e8e3f555-a73d-4955-af72-539fd5520cc4 · outbound

This paper cites Moleculenet: a benchmark for molecular machine learning,.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Moleculenet: a benchmark for molecular machine learning,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.605871Z

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Observation 0e24241f-3f05-42ee-8949-e1fb5debbadd · outbound

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

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization Open graph benchmark: Datasets for machine learning on graphs,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:45:03.596387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1a12bb70-ec9b-4d5e-b143-2cbb301fb9c0 · outbound

This paper cites OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

Enhancing Distribution and Label Consistency for Graph Out-of-Distribution Generalization OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-10T21:45:03.507093Z

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Unavailable: canonical work link unavailable.

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