Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting | Paper Citation Record

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This paper cites Learning with pseudo-ensembles.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Learning with pseudo-ensembles

Reference 1

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Observation 9b134781-6ca8-4bc0-9a81-bec4684763ad · outbound

This paper cites Evaluating robustness and uncertainty of graph models under structural dis- tributional shifts.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Evaluating robustness and uncertainty of graph models under structural dis- tributional shifts

Reference 2

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This paper cites Borgwardt, Cheng Soon Ong, Stefan Schönauer, S.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Borgwardt, Cheng Soon Ong, Stefan Schönauer, S

Reference 3

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This paper cites Introduction to Statistical Learning Theory, pages 169–207.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Introduction to Statistical Learning Theory, pages 169–207

Reference 4

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This paper cites Language models are few-shot learners.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Language models are few-shot learners

Reference 5

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

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting A simple framework for contrastive learning of visual representations

Reference 6

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This paper cites BERT: Pre-training of deep bidirectional transformers for language understanding.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting BERT: Pre-training of deep bidirectional transformers for language understanding

Reference 7

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This paper cites A closer look at distribution shifts and out-of-distribution generalization on graphs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting A closer look at distribution shifts and out-of-distribution generalization on graphs

Reference 8

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This paper cites Generalizing graph neural networks on out-of-distribution graphs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Generalizing graph neural networks on out-of-distribution graphs

Reference 9

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Observation c3ff7dac-24e4-4bec-8c00-f6f8cb969f02 · outbound

This paper cites Universal prompt tuning for graph neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Universal prompt tuning for graph neural networks

Reference 10

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This paper cites an unresolved cited work.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Unresolved cited work

Reference 11

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Observation 6cbf7e44-dd1f-4cfa-8d5a-16b1e58978a7 · outbound

This paper cites Talk like a graph: Encoding graphs for large language models.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Talk like a graph: Encoding graphs for large language models

Reference 12

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Observation 20ad9a37-55f2-49e8-8a2d-f406a7a7ccdb · outbound

This paper cites Schoenholz, Patrick F.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Schoenholz, Patrick F

Reference 13

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

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting GOOD: A graph out-of-distribution benchmark

Reference 14

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This paper cites Parameter-efficient fine- tuning for large models: A comprehensive survey.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Parameter-efficient fine- tuning for large models: A comprehensive survey

Reference 15

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

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Open graph benchmark: Datasets for machine learning on graphs

Reference 16

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This paper cites Learning discrete representations via information maximizing self-augmented training.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Learning discrete representations via information maximizing self-augmented training

Reference 17

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This paper cites PRODIGY: Enabling In-context Learning Over Graphs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting PRODIGY: Enabling In-context Learning Over Graphs

Reference 18

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This paper cites Domain adaptation without source data.IEEE Transactions on Artificial Intelligence, 2:508–518, 2021.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Domain adaptation without source data.IEEE Transactions on Artificial Intelligence, 2:508–518, 2021

Reference 19

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This paper cites Kipf and Max Welling.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Kipf and Max Welling

Reference 20

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This paper cites Understanding attention and gener- alization in graph neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Understanding attention and gener- alization in graph neural networks

Reference 21

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This paper cites Large language models are zero-shot reasoners.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Large language models are zero-shot reasoners

Reference 22

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This paper cites Temporal ensembling for semi-supervised learning.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Temporal ensembling for semi-supervised learning

Reference 23

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This paper cites Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks

Reference 24

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This paper cites The power of scale for parameter-efficient prompt tuning.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting The power of scale for parameter-efficient prompt tuning

Reference 25

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

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Ood-gnn: Out-of-distribution gen- eralized graph neural network

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:03.727622Z

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

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Learning invariant graph representations for out-of-distribution generalization

Reference 27

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This paper cites A comprehensive survey on source-free domain adaptation.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting A comprehensive survey on source-free domain adaptation

Reference 28

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This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Prefix-tuning: Optimizing continuous prompts for generation

Reference 29

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This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation

Reference 30

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This paper cites Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation

Reference 31

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This paper cites Large scale learning on non-homophilous graphs: New bench- marks and strong simple methods.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Large scale learning on non-homophilous graphs: New bench- marks and strong simple methods

Reference 32

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This paper cites Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing

Reference 33

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This paper cites Graphprompt: Unifying pre-training and downstream tasks for graph neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Graphprompt: Unifying pre-training and downstream tasks for graph neural networks

Reference 34

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This paper cites Position: Graph foundation models are already here, 2024.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Position: Graph foundation models are already here, 2024

Reference 35

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This paper cites Refram- ing instructional prompts to GPTk’s language.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Refram- ing instructional prompts to GPTk’s language

Reference 36

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This paper cites Future directions in the theory of graph machine learning, 2024.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Future directions in the theory of graph machine learning, 2024

Reference 37

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This paper cites Automatic differentiation in pytorch.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Automatic differentiation in pytorch

Reference 38

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This paper cites Geom-gcn: Geo- metric graph convolutional networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Geom-gcn: Geo- metric graph convolutional networks

Reference 39

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This paper cites Improving language understanding by generative pre-training.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Improving language understanding by generative pre-training

Reference 40

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This paper cites Language models are unsupervised multitask learners.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Language models are unsupervised multitask learners

Reference 41

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This paper cites Peters, Swabha Swayamdipta, and Thomas Wolf.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Peters, Swabha Swayamdipta, and Thomas Wolf

Reference 42

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This paper cites Regularization with stochastic trans- formations and perturbations for deep semi-supervised learning.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Regularization with stochastic trans- formations and perturbations for deep semi-supervised learning

Reference 43

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This paper cites Brenda, the enzyme database: updates and major new developments.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Brenda, the enzyme database: updates and major new developments

Reference 44

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This paper cites Understanding Machine Learning: From Theory to Algorithms.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Understanding Machine Learning: From Theory to Algorithms

Reference 45

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This paper cites Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel

Reference 46

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This paper cites Unleashing the power of graph data augmentation on covariate distribution shift.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Unleashing the power of graph data augmentation on covariate distribution shift

Reference 47

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This paper cites Gppt: Graph pre-training and prompt tuning to generalize graph neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Gppt: Graph pre-training and prompt tuning to generalize graph neural networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:01.333906Z

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This paper cites All in one: Multi-task prompting for graph neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting All in one: Multi-task prompting for graph neural networks

Reference 49

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Observation c56a0294-d8c7-4635-a253-f3eb30352a2c · outbound

This paper cites Sutherland, Lee A.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Sutherland, Lee A

Reference 50

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This paper cites Representation Learning with Contrastive Predictive Coding.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Representation Learning with Contrastive Predictive Coding

Reference 51

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This paper cites Graph attention networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Graph attention networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:00.872036Z

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 8b3115db-2567-4246-877b-94d5ba4086ea · outbound

This paper cites Freematch: Self- adaptive thresholding for semi-supervised learning.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Freematch: Self- adaptive thresholding for semi-supervised learning

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:00.688036Z

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

Observation b845fb66-32e7-4217-b75d-445875b1139f · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Chain-of-thought prompting elicits reasoning in large language models

Reference 54

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Observation 1046677f-98c7-4c40-870f-27f49d6616b5 · outbound

This paper cites Discovering invariant rationales for graph neural networks.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Discovering invariant rationales for graph neural networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:55:54.601643Z

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Observation 0b7c1538-35c3-43e6-b487-cc7fb766c850 · outbound

This paper cites Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:00.518218Z

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

Observation d26e695e-ccd9-4a6d-afeb-6159572a3f4a · outbound

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Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:56:00.382223Z

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Observation 7829155a-bf19-437b-904d-cd8140d0b221 · outbound

This paper cites Hovy, Minh-Thang Luong, and Quoc V.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Hovy, Minh-Thang Luong, and Quoc V

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:00.205463Z

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Observation 11f28749-24d3-48a6-aac9-7c1bd309db72 · outbound

This paper cites Large language models as optimizers.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Large language models as optimizers

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:56:00.024024Z

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Observation 58fc1fa9-227d-4598-8d1e-0cb9c503d976 · outbound

This paper cites Generalized out-of-distribution detection: A survey.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Generalized out-of-distribution detection: A survey

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:59.886384Z

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Observation 33aefa72-7a34-4fb8-b46c-199a7be2cd2b · outbound

This paper cites Generalized source-free domain adaptation.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Generalized source-free domain adaptation

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:59.711604Z

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Observation 20510fb2-0a90-4be7-9f6c-329dfc002dc4 · outbound

This paper cites Cohen, and Ruslan Salakhutdinov.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Cohen, and Ruslan Salakhutdinov

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:59.575414Z

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Observation 8e375dd7-4092-481f-ad20-f24bf000ce78 · outbound

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

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Gnnex- plainer: Generating explanations for graph neural networks

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:59.440074Z

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Observation e715c3a2-2566-4f6d-996b-9457fc467f31 · outbound

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Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:55:59.289550Z

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Observation d057e598-97ef-42d1-b2b5-b0925cee37f9 · outbound

This paper cites Graph contrastive learning with augmentations.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Graph contrastive learning with augmentations

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:59.081472Z

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 941c8298-f3ae-4a66-aa23-c47b5744cee5 · outbound

This paper cites Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Generalized graph prompt: Toward a unification of pre-training and downstream tasks on graphs

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:58.930446Z

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 738dbcd0-4420-4ee6-8908-42569993f595 · outbound

This paper cites Node-time conditional prompt learning in dynamic graphs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Node-time conditional prompt learning in dynamic graphs

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:58.729476Z

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 6d1895a2-a9cc-412c-9b68-9f3d0c069f67 · outbound

This paper cites Multigprompt for multi-task pre-training and prompting on graphs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Multigprompt for multi-task pre-training and prompting on graphs

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:58.542888Z

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 e073f812-dc8a-420c-9c12-95d23532c7bd · outbound

This paper cites an unresolved cited work.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Unresolved cited work

Reference 69

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:55:58.393038Z

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 10f9203d-e583-42c2-a4e9-c51db517f69a · outbound

This paper cites Graphsaint: Graph sampling based inductive learning method.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Graphsaint: Graph sampling based inductive learning method

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:58.222822Z

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 be889941-f9c7-4a76-aeb0-f26efb2df9ca · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:57.993901Z

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 bd746849-2516-4f71-bdfd-c9a09894dbd0 · outbound

This paper cites Large language models are human-level prompt engineers.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Large language models are human-level prompt engineers

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:55:56.001394Z

Source-reported events for the cited work

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Observation 6b9dbee7-2732-44e6-970a-f647c1f0b7cd · outbound

This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:57.727154Z

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 87a27c74-d7eb-4077-8836-af34d54d17c3 · outbound

This paper cites Shift-robust gnns: Overcoming the limitations of localized graph training data.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Shift-robust gnns: Overcoming the limitations of localized graph training data

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:57.533331Z

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 4981515e-6510-4d25-8ee1-0dab563e0d45 · outbound

This paper cites A comprehensive survey on transfer learning.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting A comprehensive survey on transfer learning

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:55:57.309058Z

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 e48d6489-4796-4204-90d5-1b1fc6f768be · outbound

This paper cites Prog: A graph prompt learning benchmark.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Prog: A graph prompt learning benchmark

Reference 76

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:55:57.100564Z

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 38e82049-c63e-4731-a860-6de3ed5f5c75 · outbound

This paper cites an unresolved cited work.

Freeze, Prompt, and Adapt: A Framework for Source-free Unsupervised GNN Prompting Unresolved cited work

Reference 77

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:55:56.857305Z

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.

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