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Paper Citation Record · LEDGER

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

As of 9 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 2026-08-08T06:32:00.761636+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

No source-named external measurement is stored.

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=sha256:070bbe827f760e14f56742c4caae7e2fcb569715d0c5b88ac9a1f44246b5c2ed

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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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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source=arxiv_source observed=2026-08-01T13:29:50.868342Z digest=sha256:b4a1a836b5f26dcc0fd2f6d19b6b29f69b1852545ef3f27e05475b4e3508814d

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

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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

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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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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

This paper cites 2021 , eprint=.

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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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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source=arxiv_source observed=2026-08-01T13:29:54.497539Z digest=sha256:9c06fa561b4c777d5b5da18c4c6d0732533669c223781d94f82e5bf0a19bd371

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=sha256:14008133f7cc24f753faf06b733ba557d20b11cbaea5f97db07b591f315efcbb

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

This paper cites One for All: Towards Training One Graph Model for All Classification Tasks.

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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source=arxiv_source observed=2026-08-01T13:29:54.692228Z digest=sha256:c514e4e0892480e547aebd2a28d2c1606a16eeac7abd781f31a404bc1ace09d6

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=sha256:6dc4317dc5bbaae0df4ca62ad74528fd5074ffb078d8a8352ba490ee2837e85c

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:e80bec8c2c78ab213aca72fe0555d6fb86db97bfb9742e36a59bae7224a7afe7

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:a6831a5d96949782e5aa983447eb27985f0fe5a6648efc83c8d9a92da3d1dccd

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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source=arxiv_source observed=2026-08-01T13:29:54.974893Z digest=sha256:086e67ced9df5ec82c49d7d104a0f7d6f413a0fe90a5d2c728ef1e7c0968f779

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-01T13:29:55.087948Z digest=sha256:6a0ea92ea2b17f6b286ba07e016d272b2334c20cae48ef5cf893e5e4709f5aba

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:ed47ea296e48edc059c15ca05f09669892e79f276cd9d8ec13fb67e69351f9de

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:d1dbfdff3f9ef249c4d01e8406a9462fa00831bdeec87c08a139bc9b102d1cd1

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=sha256:63f1e75d14420b6f4d2fa0f1be42f07df2a4d8455289e231761241b98e4c9725

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:b7435d2526cd056a7ad6c99bdb3dadb6e87b69bef400ccd56759f7d43e505ddb

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=sha256:329670a9b4099d6c65dde208514de4640ecd26b5a4f792c99c1d8bb4002cdb4a

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=sha256:5db049c8bb207cf8d6e13de9fca56d5368a7bcde4756616d1bc67945018dbed7

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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source=arxiv_source observed=2026-08-01T13:29:55.974365Z digest=sha256:116a7ea70ac83d34f246b8ddf7157fc541d694d32948c1d8187e96006dbe0bfd

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=sha256:8e566fe3ac7d77a443c9c39d82fd0db98c3ad9e8b9df1a14bd553bedc1c7f4d2

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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source=arxiv_source observed=2026-08-01T13:29:56.261064Z digest=sha256:b8f3cecba813ded0d2f38e480234ae8e86cf543f9acf3a7c651240208af0bfb5

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=sha256:9eeeeeed5f001528de3c2a1d2cf3716f268fe3da8138e12154074536750fc1a4

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:a5d5138937eea509bb6b79975f9bec142c86ebe0e79631193f1c8c99fd9f8d71

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=sha256:2a2120506392f2101f993db14dab4ab9394620bb7e7d4f640f25bdd21e24b504

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:a284bad6bc458861ce856cb274c2d6542d6a6d8b016c118607d6def37170523b

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:c72e54bcf930031cba5cf862c17589739dd7c0429c09d9d043ea26d85d6f4c6d

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:afc29d6be6d554cff50cd1e4aac142a7421b009a074bb69bcc7b696154e48206

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:e7418dd442b46781259abe111ac75b8b1322c55ddf43eca0c874c2c05cca4774

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:ac16ba4839dea047ae5a3a837aae8e86a1b9a66c668ede3bf4aa91e1bc3f7864

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:dc12d9ecc2e1cbc0d1beee15020709f6af66a5d3e46834dac72e2d1a6f124f5d

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=sha256:94eabaa9e010b1fb1780cb0ab874d2ba789219abee03f0d48b69c80dca988213

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=sha256:37fc8e6fdd3a4bfa11683d821debca22eec5bce353682fa00b3deb0dbda8d256

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:ed4b032713c20d9a1598735227468cf81eb6f543505f4ab8e67211f3238a8424

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:e4d0f5d0e69a24e15babcca5779259120c7217ed996df1fe249f3d0661d81e11

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=sha256:2678822bd60b5b6a1b5a8899165859069220fa4d4d90f12ec5d8913e9255946a

Pith citing papers

No inbound Pith citation observations are available.