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Paper Citation Record · LEDGER

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation

As of 13 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 0 inbound Pith citation observations for arXiv:2607.19108.

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

pith.paper-citation-record.v1
2607.19108 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T13:29:58.086788Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

68 of 68 outbound references displayed

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External citation measurements

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Outbound references

Observation a04e584a-59ae-49de-b86b-4ab00787c2e0 · outbound

This paper cites Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Communication, Simulation, and Intelligent Agents: Implications of Personal Intelligent Machines for Medical Education

Reference 1

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source=arxiv_source observed=2026-08-01T13:29:50.595550Z digest=sha256:3cf0e9356b9548f0451ccc126907031ca46f7edef7083147db275d30395602b0

Observation 5181571e-acba-4874-ad46-741b12dde99b · outbound

This paper cites Classification Problem Solving.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Classification Problem Solving

Reference 2

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source=arxiv_source observed=2026-08-01T13:29:50.714001Z digest=sha256:fd003d9b4f375352cbb5c8d76cb4c1a29f5bc9043284e6dfa3f428ccf9f24f9a

Observation 8e689d57-b0b4-4785-b8df-9e9a3ad1fe0c · outbound

This paper cites , title =.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation , title =

Reference 3

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Observation 29f1e72b-ce2c-4abd-9e41-4f7f379ee116 · outbound

This paper cites New Ways to Make Microcircuits Smaller---Duplicate Entry.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation New Ways to Make Microcircuits Smaller---Duplicate Entry

Reference 4

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Observation 89e92158-77bb-4b2c-9d42-29f9078c0567 · outbound

This paper cites Clancey and Glenn Rennels , abstract =.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Clancey and Glenn Rennels , abstract =

Reference 5

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Observation 4bc97787-4ae6-40f2-88a4-276d8965fac9 · outbound

This paper cites and Rennels, Glenn R.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation and Rennels, Glenn R

Reference 6

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Observation 208fb90b-1f73-4034-9902-b43f8e2dc85a · outbound

This paper cites Poligon: A System for Parallel Problem Solving.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Poligon: A System for Parallel Problem Solving

Reference 7

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Observation 0f8df6d2-de2f-4c9d-b83a-f2661d880832 · outbound

This paper cites Transfer of Rule-Based Expertise through a Tutorial Dialogue.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Transfer of Rule-Based Expertise through a Tutorial Dialogue

Reference 8

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Observation b5e431ba-ff19-4fe4-ab54-72721a700d9a · outbound

This paper cites The Engineering of Qualitative Models.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation The Engineering of Qualitative Models

Reference 9

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Observation b0b2c228-5126-477a-93f3-cc3a0743bc15 · outbound

This paper cites 2023 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2023 , eprint=

Reference 10

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Observation e5ef28ab-198d-4ecb-b463-22321e84b386 · outbound

This paper cites Pluto: The 'Other' Red Planet.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Pluto: The 'Other' Red Planet

Reference 11

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Observation 2ccf2f3a-9977-4e35-ac7c-61b998792f0d · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Advances in Neural Information Processing Systems , volume=

Reference 12

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Observation 9bdfff38-6925-4e02-b511-314369105971 · outbound

This paper cites Graph Learning in the Era of.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Graph Learning in the Era of

Reference 13

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Observation 33a04f7a-11bb-4c0b-b5b3-e99ba88cd43a · outbound

This paper cites Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Reference 14

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Observation 8f85cb3f-91f8-4605-be8e-a48e2217f8b9 · outbound

This paper cites A Survey on Graph Structure Learning: Progress and Opportunities.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation A Survey on Graph Structure Learning: Progress and Opportunities

Reference 15

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Observation 5fe6c8e6-fbad-4f6e-8d76-a59f736834c8 · outbound

This paper cites Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?

Reference 16

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Observation a6191c60-2b38-4516-b010-380d96afe04b · outbound

This paper cites Rethinking Graph Structure Learning in the Era of.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Rethinking Graph Structure Learning in the Era of

Reference 17

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Observation 96f6b722-5bd9-4d7d-9051-8c83843e521c · outbound

This paper cites an unresolved cited work.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Unresolved cited work

Reference 18

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Observation ab0c0ed4-1a3f-4beb-a319-c7c33e46941b · outbound

This paper cites Harnessing Explanations:.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Harnessing Explanations:

Reference 19

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Observation b3523cc4-b9c7-4ca5-9456-02805d4926d2 · outbound

This paper cites Companion Proceedings of the ACM Web Conference 2024 , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Companion Proceedings of the ACM Web Conference 2024 , year=

Reference 20

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Observation b8ea8e6d-6b60-4cf5-b0f9-b6224ad640d5 · outbound

This paper cites 2024 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2024 , eprint=

Reference 21

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Observation afe194b9-7789-45b0-b659-5f7a9ebc172f · outbound

This paper cites Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , year=

Reference 22

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Observation 56da4ef9-5342-4efd-83ec-ed52279776fc · outbound

This paper cites 2023 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2023 , eprint=

Reference 23

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source=arxiv_source observed=2026-08-01T13:29:52.866889Z digest=sha256:d98f108350f431953c1bf9b8c51e945b7aab9ea1b2ccec98cc3bc465cc080fbb

Observation 0c988886-6fbb-407c-8ecb-f15e8e818b44 · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation IEEE Transactions on Pattern Analysis and Machine Intelligence , year=

Reference 24

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Observation a8e80baa-2eae-4e1d-b89d-11e338f23aa8 · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 25

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Observation 6f5071f7-5112-4752-83ce-b683eea89d5c · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2021 , eprint=

Reference 26

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Observation 527bdd1a-ecd6-4d47-9d48-5422dfda2106 · outbound

This paper cites NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise

Reference 27

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Observation 34998482-615f-4261-a1e4-b3ea1fcd8ce6 · outbound

This paper cites IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning

Reference 28

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Observation e3faa0d5-1710-4e53-b31a-99046a39bcf8 · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2025 , eprint=

Reference 29

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Observation 7e5301fa-ca6a-4ffb-888d-a105210ec25b · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Semi-Supervised Classification with Graph Convolutional Networks

Reference 30

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Observation ddfb62c6-18f6-459f-b3aa-7955b006466c · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Graph Attention Networks

Reference 31

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Observation 12496650-64b2-4ef9-a976-03c1170dfb0a · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Advances in Neural Information Processing Systems , year=

Reference 32

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Observation a2095ce1-13c9-4316-a3fa-cae450c3b89a · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation International Conference on Learning Representations , year=

Reference 33

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Observation 1a89a488-396c-4adf-b019-6444d0844813 · outbound

This paper cites Efficient Tuning and Inference for Large Language Models on Textual Graphs.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Efficient Tuning and Inference for Large Language Models on Textual Graphs

Reference 34

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Observation 29476126-1362-4191-baf1-5152f708d24a · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , year=

Reference 35

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Observation 9b135562-c291-4921-bd9f-e273c8cbad24 · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Can GNN be Good Adapter for LLMs?

Reference 36

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Observation e3cfb5b0-847e-410a-8fdd-32318476422a · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2023 , eprint=

Reference 37

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source=arxiv_source observed=2026-08-01T13:29:54.558802Z digest=sha256:7d2055df86e2a93fac0d8d565ffa7995c20dce8ccdcc6b138dad32fe07753ab8

Observation c59ff686-bab1-41c0-8da5-3539228d178c · outbound

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OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation One for All: Towards Training One Graph Model for All Classification Tasks

Reference 38

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Observation 00edca19-4d1b-4e1b-b19c-c2f579afb9e6 · outbound

This paper cites Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages=

Reference 39

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Observation 71799542-4e9b-4a9f-8896-3d663611c3a4 · outbound

This paper cites Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

Reference 40

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Observation 4ad88021-3b66-4c8b-ba31-fa2cca3b2951 · outbound

This paper cites Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Findings of the Association for Computational Linguistics: EMNLP 2024 , pages=

Reference 41

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source=arxiv_source observed=2026-08-01T13:29:54.742785Z digest=sha256:2c2862bc2a680e43135b935ff0477c4e47071677403c1de0b23679e0d55750d8

Observation 7b9d8097-36de-455c-ba15-f47325ddb40f · outbound

This paper cites 2025 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2025 , eprint=

Reference 42

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source=arxiv_source observed=2026-08-01T13:29:54.776588Z digest=sha256:84cae53fab8ff832b9e2d672e9922ca045f44b1ffe113e932f6f10a935d4923c

Observation 6268c423-a6f9-4581-8392-16098bae73e0 · outbound

This paper cites Contextual Text Denoising with Masked Language Models.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Contextual Text Denoising with Masked Language Models

Reference 43

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source=arxiv_source observed=2026-08-01T13:29:54.834570Z digest=sha256:bf7f2230e3f240540fe1bb88d4e21b8c91e1569a905ca2b81cc80f433d56195e

Observation a6793ee5-88ff-45bb-a15e-23bba9a49d1f · outbound

This paper cites Denoising based Sequence-to-Sequence Pre-training for Text Generation.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Denoising based Sequence-to-Sequence Pre-training for Text Generation

Reference 44

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Observation ced5461c-b6bb-4bc3-80b9-1e1ef269c4e1 · outbound

This paper cites A Text Normalisation System for Non-Standard.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation A Text Normalisation System for Non-Standard

Reference 45

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verified exact
doi, observed 2026-08-01T13:33:55.064334Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-01T13:29:55.087948Z digest=sha256:db0b2fe13455bac53d9d707186bd8eb2bee57f8536f6e2a936808585bfaef544

Observation 8482fb05-8a2e-4dab-a745-ab6e255494af · outbound

This paper cites Natural Language Generation , year =.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Natural Language Generation , year =

Reference 46

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source=arxiv_source observed=2026-08-01T13:29:55.230005Z digest=sha256:b4744efe24b272fe4a4a704df2cd24b8f1943d02a491926d28ed51ce05b3da22

Observation dd687a10-3355-437d-9391-7baf9fd8ec35 · outbound

This paper cites 2019 , pages =.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2019 , pages =

Reference 47

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source=arxiv_source observed=2026-08-01T13:29:55.326269Z digest=sha256:00482a6aabd807741abf9b9d381474affd0ddf90d7effcff9f65dbf3072d4154

Observation 3de31c5a-a4e6-41d9-a342-fe344cd0a598 · outbound

This paper cites GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models

Reference 48

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source=arxiv_source observed=2026-08-01T13:29:55.438619Z digest=sha256:3028827fe2253cf07fbbdc4cb961aa153cbe58a4ec4d28c2bf91ee536528f941

Observation 1df0f002-6388-489b-a6af-295f51f654c1 · outbound

This paper cites IEEE Transactions on Neural Networks and Learning Systems , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation IEEE Transactions on Neural Networks and Learning Systems , year=

Reference 49

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source=arxiv_source observed=2026-08-01T13:29:55.538873Z digest=sha256:0f162b43a8042482626f058427fb3c25cd39447598491def394a98bf51dd1144

Observation 637ac7e7-7a0b-4706-8d88-cca0bac3a94d · outbound

This paper cites SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization

Reference 50

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source=arxiv_source observed=2026-08-01T13:29:55.647413Z digest=sha256:2728596e7d6c8dbad8bf23490d9621a60599ef5d67d41cafb54715ac8fdaa149

Observation 94e78cfb-f472-4a92-bdf0-e9757a66330d · outbound

This paper cites 2022 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2022 , eprint=

Reference 51

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source=arxiv_source observed=2026-08-01T13:29:55.797541Z digest=sha256:be0bded899509849fbe2528ca08e7aa88812c26083aa91bed71712c5d8abea24

Observation 43d2e605-d74a-4226-87d0-80ffe0fb7125 · outbound

This paper cites Towards Unsupervised Deep Graph Structure Learning.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Towards Unsupervised Deep Graph Structure Learning

Reference 52

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Observation 21c30b6c-36f4-46ea-b49b-cdd5070f47dc · outbound

This paper cites GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation GraphPatcher: Mitigating Degree Bias for Graph Neural Networks via Test-time Augmentation

Reference 53

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source=arxiv_source observed=2026-08-01T13:29:56.128491Z digest=sha256:2d6db6afb271f489a7e2eb54c7b4ab87a8681d2f402d42da9e3c8567b1b6538d

Observation 7e81dd84-e729-4566-9e66-c6e4110bf7fa · outbound

This paper cites GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node Classification

Reference 54

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Observation c563a9f4-4b01-4904-983d-e5af90ddc90d · outbound

This paper cites LTE4G: Long-Tail Experts for Graph Neural Networks.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation LTE4G: Long-Tail Experts for Graph Neural Networks

Reference 55

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source=arxiv_source observed=2026-08-01T13:29:56.395987Z digest=sha256:c8ff9b444d3e9e70514ead1400b7866352dde7604e852627946c0453607c5faf

Observation 92f2d833-252c-44ba-b40c-c888d6643615 · outbound

This paper cites Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , year=

Reference 56

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source=arxiv_source observed=2026-08-01T13:29:56.514465Z digest=sha256:6f02c57cc449b8cbea2209d81d261439c93ee8f7924998c6c550c10bde0c604a

Observation 03ef6850-93eb-4127-94d1-4c4d38136acb · outbound

This paper cites TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification

Reference 57

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source=arxiv_source observed=2026-08-01T13:29:56.675226Z digest=sha256:4e98202712200033cd8519c7e7e23a43ff710cad935125626524d6c429515f8c

Observation e2026d9c-3d22-4694-9093-6c59cd105b15 · outbound

This paper cites Proceedings of the 30th ACM International Conference on Multimedia , pages=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the 30th ACM International Conference on Multimedia , pages=

Reference 58

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source=arxiv_source observed=2026-08-01T13:29:56.807708Z digest=sha256:753a639aefc7a6fa962e21e3189431661f1b7e01ef405cc5d793613943f4ffee

Observation cacbe7d4-2b57-4252-8563-cfc50bf18820 · outbound

This paper cites GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution Assignment

Reference 59

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source=arxiv_source observed=2026-08-01T13:29:56.915053Z digest=sha256:94371868adaf5fcec6b6f3754dd2119c2118f311dab46a6e33dc4da94727ef75

Observation dd65f05c-ccf8-42c5-adef-50e89fea23aa · outbound

This paper cites 2021 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2021 , eprint=

Reference 60

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source=arxiv_source observed=2026-08-01T13:29:56.996594Z digest=sha256:a8f963b6df91db511695ea3968c298d6f6cc058689de55f7594e301b61688b81

Observation 8638cb88-1128-4501-9033-97af44ed0fbd · outbound

This paper cites Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions

Reference 61

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source=arxiv_source observed=2026-08-01T13:29:57.109558Z digest=sha256:7def1598dff63410e558669c02803e9558dd3923c5e1e800bc57959198b15202

Observation 18c68a3e-6a3c-4289-9de8-9ec78c0bffde · outbound

This paper cites Robust Training of Graph Neural Networks via Noise Governance.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Robust Training of Graph Neural Networks via Noise Governance

Reference 62

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source=arxiv_source observed=2026-08-01T13:29:57.250025Z digest=sha256:dbbb32e869efc89273106a318fbe3d6248da93d9c66ceff9bebd09c26c682689

Observation 09de08f3-6f8b-4ada-a8d1-8bc3aa72ab22 · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the AAAI Conference on Artificial Intelligence , year=

Reference 63

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source=arxiv_source observed=2026-08-01T13:29:57.375099Z digest=sha256:7b07a4a0852033ad5a57ea999061b66f829a6d18f5a4967cd532e554be5fc916

Observation 8ac5c8f1-a760-425a-bc0a-14d24b43e3c2 · outbound

This paper cites International Conference on Machine Learning , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation International Conference on Machine Learning , year=

Reference 64

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source=arxiv_source observed=2026-08-01T13:29:57.508577Z digest=sha256:f7dc78cf3f04a0ea3da94413b6dee79ff7a4b5e49da67ebc2b6d9ff56a03df29

Observation 72f0c56e-1aa7-4fd7-a8b2-79ee5de4a263 · outbound

This paper cites Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

Reference 65

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source=arxiv_source observed=2026-08-01T13:29:57.675045Z digest=sha256:331b01360192d05e4f3a77c88b54dc3f791da48700107e6d8168e931a7da9e47

Observation c44b83c2-1b25-4b42-a18a-b80c00bc7371 · outbound

This paper cites Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval , year=

Reference 66

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source=arxiv_source observed=2026-08-01T13:29:57.800561Z digest=sha256:c8d77d2b3a24265735c1a560f852ad0dfc09ea224e1b68cbf18132c70f00bbbf

Observation 29ee90d3-68ee-4b97-8ace-5e3cea2e89dc · outbound

This paper cites Advances in Neural Information Processing Systems Datasets and Benchmarks Track , year=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation Advances in Neural Information Processing Systems Datasets and Benchmarks Track , year=

Reference 67

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source=arxiv_source observed=2026-08-01T13:29:57.922395Z digest=sha256:843e888df1cb24937ab9cfa1396397c6490187d08afa3f9e793a0037ec1691e3

Observation ac7d5351-2957-4367-bd74-f87f6a932e35 · outbound

This paper cites 2024 , eprint=.

OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradation 2024 , eprint=

Reference 68

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source=arxiv_source observed=2026-08-01T13:29:58.086788Z digest=sha256:672eb669f9602d8a8066047146b2cfa9e2c6c83b9e19f6587687f68b1838ac87

Pith citing papers

No inbound Pith citation observations are available.