Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark | Paper Citation Record

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Tsang Masashi Sugiyama Bo Han, Quanming Yao

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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This paper cites InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.

Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark InProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Fast Graph Representation Learning with PyTorch Geometric

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Understanding the Effect of Noise in LLM Training Data with Algorithmic Chains of Thought

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Semi-Supervised Classification with Graph Convolutional Networks

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Selective Fine-tuning on LLM-labeled Data May Reduce Reliance on Human Annotation: A Case Study Using Schedule-of-Event Table Detection

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark DivideMix: Learning with Noisy Labels as Semi-supervised Learning

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark One for All: Towards Training One Graph Model for All Classification Tasks

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Resilience of Large Language Models for Noisy Instructions

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Pitfalls of Graph Neural Network Evaluation

Reference 59

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

Reference 60

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise

Reference 61

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

Reference 62

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

Reference 63

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

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Reference 70

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Reference 72

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Graph Neural Networks for Graphs with Heterophily: A Survey

Reference 73

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Reference 75

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Children

Reference 76

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Unresolved cited work

Reference 77

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark InICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Reference 79

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Information Retrieval3 (2000), 127–163

Reference 2000

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark InProceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining

Reference 2019

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Geom-GCN: Geometric Graph Convolutional Networks

Reference 2020

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 2021

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark InProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

Reference 2023

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Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark Can LLMs Replace Manual Annotation of Software Engineering Artifacts?

Reference 2024

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