Subgraph Generation for Generalizing on Out-of-Distribution Links | Paper Citation Record

Observation 657cc74d-d1c0-4882-8c6f-f3c3ac734c22 · outbound

This paper cites Kipf and Max Welling.

Subgraph Generation for Generalizing on Out-of-Distribution Links Kipf and Max Welling

Reference 1

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This paper cites The link prediction problem for social networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links The link prediction problem for social networks

Reference 2

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This paper cites Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking.

Subgraph Generation for Generalizing on Out-of-Distribution Links Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking

Reference 3

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Observation da12c437-597e-4f7e-8df7-ecc300213cb7 · outbound

This paper cites Variational Graph Auto-Encoders.

Subgraph Generation for Generalizing on Out-of-Distribution Links Variational Graph Auto-Encoders

Reference 4

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This paper cites Neural common neighbor with completion for link prediction.

Subgraph Generation for Generalizing on Out-of-Distribution Links Neural common neighbor with completion for link prediction

Reference 5

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This paper cites Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction.

Subgraph Generation for Generalizing on Out-of-Distribution Links Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction

Reference 6

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Observation b9a83937-53b3-4aa0-9158-c3a648e39acb · outbound

This paper cites Lpformer: an adaptive graph transformer for link prediction.

Subgraph Generation for Generalizing on Out-of-Distribution Links Lpformer: an adaptive graph transformer for link prediction

Reference 7

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Observation 0af5290c-ccca-4321-8a13-17efe8fe187e · outbound

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Good: A graph out-of-distribution benchmark

Reference 8

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Observation d6ddd7ae-4c58-42bc-a407-f0ab65f313ce · outbound

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Ood-gnn: Out-of-distribution generalized graph neural network

Reference 9

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Observation 364363b3-4957-44a9-bd9c-21983b6d37ef · outbound

This paper cites Alleviating structural distribution shift in graph anomaly detection.

Subgraph Generation for Generalizing on Out-of-Distribution Links Alleviating structural distribution shift in graph anomaly detection

Reference 10

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Observation d5b8ac85-db2d-4b10-ba1b-b40dbd9e8357 · 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.

Subgraph Generation for Generalizing on Out-of-Distribution Links DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations

Reference 11

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Observation 3a1891e1-62af-44f1-96f2-631e6c7db228 · outbound

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Wilds: A benchmark of in-the-wild distribution shifts

Reference 12

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Observation e2fa89fd-8d01-4f23-86d1-3d0a631c5021 · outbound

This paper cites Ood link prediction generalization capabilities of message-passing gnns in larger test graphs.Advances in Neural Information Processing Systems, 35:20257– 20272, 2022.

Subgraph Generation for Generalizing on Out-of-Distribution Links Ood link prediction generalization capabilities of message-passing gnns in larger test graphs.Advances in Neural Information Processing Systems, 35:20257– 20272, 2022

Reference 13

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Observation 7d086b96-7639-4813-99b6-87197ee57c70 · outbound

This paper cites Size-invariant graph representations for graph classification extrapolations.

Subgraph Generation for Generalizing on Out-of-Distribution Links Size-invariant graph representations for graph classification extrapolations

Reference 14

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This paper cites Towards Understanding Link Predictor Generalizability Under Distribution Shifts.

Subgraph Generation for Generalizing on Out-of-Distribution Links Towards Understanding Link Predictor Generalizability Under Distribution Shifts

Reference 15

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Observation 52546bb0-dcf4-48a5-96c8-d85d6eb86aa5 · outbound

This paper cites Invariant Risk Minimization.

Subgraph Generation for Generalizing on Out-of-Distribution Links Invariant Risk Minimization

Reference 16

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Observation b0b9815f-f18b-43d4-8096-e64fa06013a9 · outbound

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Out-of-distribution generalization via risk extrapolation (rex)

Reference 17

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This paper cites Graph out-of-distribution generalization via causal intervention.

Subgraph Generation for Generalizing on Out-of-Distribution Links Graph out-of-distribution generalization via causal intervention

Reference 18

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Observation 922c1d73-8c7f-4fea-b3c4-49437a39dc79 · outbound

This paper cites Tent: Fully Test-time Adaptation by Entropy Minimization.

Subgraph Generation for Generalizing on Out-of-Distribution Links Tent: Fully Test-time Adaptation by Entropy Minimization

Reference 19

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Observation f2588709-0dcd-4b53-8458-4f8959a1bb7f · outbound

This paper cites Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection.

Subgraph Generation for Generalizing on Out-of-Distribution Links Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection

Reference 20

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This paper cites Meta ood learning for continuously adaptive ood detection.

Subgraph Generation for Generalizing on Out-of-Distribution Links Meta ood learning for continuously adaptive ood detection

Reference 21

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This paper cites Energy-based Out-of-Distribution Detection for Graph Neural Networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links Energy-based Out-of-Distribution Detection for Graph Neural Networks

Reference 22

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Observation 7da4fbd6-6b6b-467c-885c-86e0fa89459b · outbound

This paper cites Counterfactual reason- ing for out-of-distribution multimodal sentiment analysis.

Subgraph Generation for Generalizing on Out-of-Distribution Links Counterfactual reason- ing for out-of-distribution multimodal sentiment analysis

Reference 23

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This paper cites Clear: Generative counter- factual explanations on graphs.

Subgraph Generation for Generalizing on Out-of-Distribution Links Clear: Generative counter- factual explanations on graphs

Reference 24

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Observation 800cf6b2-c54f-45a6-a9c8-ef7b6bf26e22 · outbound

This paper cites Learning from counterfactual links for link prediction.

Subgraph Generation for Generalizing on Out-of-Distribution Links Learning from counterfactual links for link prediction

Reference 25

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This paper cites Labeling trick: A theory of using graph neural networks for multi-node representation learning.

Subgraph Generation for Generalizing on Out-of-Distribution Links Labeling trick: A theory of using graph neural networks for multi-node representation learning

Reference 26

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Observation 3d9ba654-ff0f-49a6-9b7e-210f266878df · outbound

This paper cites On the equivalence between positional node embeddings and structural graph representations.

Subgraph Generation for Generalizing on Out-of-Distribution Links On the equivalence between positional node embeddings and structural graph representations

Reference 27

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Observation 45779a5f-b7ba-4609-a4a8-938f9763cf72 · outbound

This paper cites Link prediction based on graph neural networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links Link prediction based on graph neural networks

Reference 28

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This paper cites Neural bellman-ford networks: A general graph neural network framework for link prediction.

Subgraph Generation for Generalizing on Out-of-Distribution Links Neural bellman-ford networks: A general graph neural network framework for link prediction

Reference 29

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Observation 89638f6b-7e59-441a-a823-295d81b01456 · outbound

This paper cites Graph Neural Networks for Link Prediction with Subgraph Sketching.

Subgraph Generation for Generalizing on Out-of-Distribution Links Graph Neural Networks for Link Prediction with Subgraph Sketching

Reference 30

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This paper cites Demystifying structural disparity in graph neural networks: Can one size fit all? Advances in Neural Information Processing Systems, 36, 2024.

Subgraph Generation for Generalizing on Out-of-Distribution Links Demystifying structural disparity in graph neural networks: Can one size fit all? Advances in Neural Information Processing Systems, 36, 2024

Reference 31

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Graphrnn: Generating realistic graphs with deep auto-regressive models

Reference 32

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This paper cites DiGress: Discrete Denoising diffusion for graph generation.

Subgraph Generation for Generalizing on Out-of-Distribution Links DiGress: Discrete Denoising diffusion for graph generation

Reference 33

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This paper cites Graphdf: A discrete flow model for molecular graph generation.

Subgraph Generation for Generalizing on Out-of-Distribution Links Graphdf: A discrete flow model for molecular graph generation

Reference 34

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This paper cites Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators.

Subgraph Generation for Generalizing on Out-of-Distribution Links Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators

Reference 35

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This paper cites Autore- gressive diffusion model for graph generation.

Subgraph Generation for Generalizing on Out-of-Distribution Links Autore- gressive diffusion model for graph generation

Reference 36

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This paper cites Deep coral: Correlation alignment for deep domain adaptation.

Subgraph Generation for Generalizing on Out-of-Distribution Links Deep coral: Correlation alignment for deep domain adaptation

Reference 37

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization

Reference 38

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This paper cites Domain-adversarial training of neural networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links Domain-adversarial training of neural networks

Reference 39

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This paper cites In Search of Lost Domain Generalization.

Subgraph Generation for Generalizing on Out-of-Distribution Links In Search of Lost Domain Generalization

Reference 40

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Learning invariant graph representations for out-of-distribution generalization

Reference 41

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This paper cites Does invariant graph learning via environment augmentation learn invariance? Advances in Neural Information Processing Systems, 36:71486–71519, 2023.

Subgraph Generation for Generalizing on Out-of-Distribution Links Does invariant graph learning via environment augmentation learn invariance? Advances in Neural Information Processing Systems, 36:71486–71519, 2023

Reference 42

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Observation 3b71f14d-6939-44d2-bf67-f0bda36bec8a · outbound

This paper cites Dynamic graph neural networks under spatio-temporal distribution shift.

Subgraph Generation for Generalizing on Out-of-Distribution Links Dynamic graph neural networks under spatio-temporal distribution shift

Reference 43

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Observation 61638a94-4fd5-42e4-842f-3282b005df14 · outbound

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Handling distribution shifts on graphs: An invariance perspective

Reference 44

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Observation 8d0db294-f7e7-4f58-9316-61eb870a18e0 · outbound

This paper cites GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation.

Subgraph Generation for Generalizing on Out-of-Distribution Links GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation

Reference 45

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This paper cites Fast unfolding of communities in large networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links Fast unfolding of communities in large networks

Reference 46

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This paper cites Edge Proposal Sets for Link Prediction.

Subgraph Generation for Generalizing on Out-of-Distribution Links Edge Proposal Sets for Link Prediction

Reference 47

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This paper cites Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020.

Subgraph Generation for Generalizing on Out-of-Distribution Links Generative adversarial networks.Communications of the ACM, 63(11):139– 144, 2020

Reference 48

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This paper cites Conditional structure generation through graph variational generative adversarial nets.

Subgraph Generation for Generalizing on Out-of-Distribution Links Conditional structure generation through graph variational generative adversarial nets

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:10:53.435085Z

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This paper cites Clustering and preferential attachment in growing networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links Clustering and preferential attachment in growing networks

Reference 50

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This paper cites A new status index derived from sociometric analysis.

Subgraph Generation for Generalizing on Out-of-Distribution Links A new status index derived from sociometric analysis

Reference 51

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This paper cites Graphvae: Towards generation of small graphs using variational autoencoders.

Subgraph Generation for Generalizing on Out-of-Distribution Links Graphvae: Towards generation of small graphs using variational autoencoders

Reference 52

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Observation c61bb0be-4b1f-4787-ae40-ad6e923bab21 · outbound

This paper cites Swingnn: Rethinking permutation invariance in diffusion models for graph generation.

Subgraph Generation for Generalizing on Out-of-Distribution Links Swingnn: Rethinking permutation invariance in diffusion models for graph generation

Reference 53

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Observation 6691d052-624d-46e2-9b00-05b64e7a1fe4 · outbound

This paper cites Semi-implicit variational inference.

Subgraph Generation for Generalizing on Out-of-Distribution Links Semi-implicit variational inference

Reference 54

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Observation 06487935-7732-4f55-b5b8-448ec85889d9 · outbound

This paper cites Semi-implicit graph variational auto-encoders.

Subgraph Generation for Generalizing on Out-of-Distribution Links Semi-implicit graph variational auto-encoders

Reference 55

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This paper cites Investigating and mitigating degree-related biases in graph convoltuional networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links Investigating and mitigating degree-related biases in graph convoltuional networks

Reference 56

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This paper cites On generalized degree fairness in graph neural networks.

Subgraph Generation for Generalizing on Out-of-Distribution Links On generalized degree fairness in graph neural networks

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T17:10:52.287231Z

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This paper cites Adversarially Regularized Graph Autoencoder for Graph Embedding.

Subgraph Generation for Generalizing on Out-of-Distribution Links Adversarially Regularized Graph Autoencoder for Graph Embedding

Reference 58

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

Subgraph Generation for Generalizing on Out-of-Distribution Links Open graph benchmark: Datasets for machine learning on graphs

Reference 59

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This paper cites directions.

Subgraph Generation for Generalizing on Out-of-Distribution Links directions

Reference 60

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