mlsanityData to 14 Oct 2026

Text Level Graph Neural Network for Text Classification

arXiv 1910.02356 ↗First posted 6 Oct 2019cs.CLcited by 60

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Abstract (arXiv)

Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information. However, previous methods based on GNN are mainly faced with the practical problems of fixed corpus level graph structure which do not support online testing and high memory consumption. To tackle the problems, we propose a new GNN based model that builds graphs for each input text with global parameters sharing instead of a single graph for the whole corpus. This method removes the burden of dependence between an individual text and entire corpus which support online testing, but still preserve global information. Besides, we build graphs by much smaller windows in the text, which not only extract more local features but also significantly reduce the edge numbers as well as memory consumption. Experiments show that our model outperforms existing models on several text classification datasets even with consuming less memory.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Oct 2019.

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Citing papers per year: 2021: 13; 2022: 18; 2023: 11; 2024: 10; 2025: 5; 2026: 3.

60 papers.

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  1. 2022Learning on Large-scale Text-attributed Graphs via Variational Inferencecited by 155
  2. 2021Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Predictioncited by 138
  3. 2021BertGCN: Transductive Text Classification by Combining GCN and BERTcited by 74
  4. 2023SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learningcited by 73
  5. 2022Multimodal learning with graphscited by 54
  6. 2021ReGVD: Revisiting Graph Neural Networks for Vulnerability Detectioncited by 32
  7. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  8. 2023Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphscited by 29
  9. 2022CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detectioncited by 21
  10. 2022Test-Time Training for Graph Neural Networkscited by 17
  11. 2021Graph Classification by Mixture of Diverse Expertscited by 16
  12. 2021Sparse Structure Learning via Graph Neural Networks for Inductive Document Classificationcited by 15
38 more
  1. 2022InducT-GCN: Inductive Graph Convolutional Networks for Text Classificationcited by 15
  2. 2023Graph Neural Networks for Text Classification: A Surveycited by 15
  3. 2023Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representationcited by 14
  4. 2021Hierarchical Heterogeneous Graph Representation Learning for Short Text Classificationcited by 13
  5. 2021An Adaptive Graph Pre-training Framework for Localized Collaborative Filteringcited by 8
  6. 2023Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Biascited by 7
  7. 2024Learning to Approximate Adaptive Kernel Convolution on Graphscited by 6
  8. 2022TextConvoNet:A Convolutional Neural Network based Architecture for Text Classificationcited by 5
  9. 2023An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graphcited by 5
  10. 2024Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classificationcited by 5
  11. 2022Understanding Graph Convolutional Networks for Text Classificationcited by 4
  12. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  13. 2022Explicit Feature Interaction-aware Graph Neural Networkscited by 3
  14. 2022Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognitioncited by 3
  15. 2022Heterogeneous Graph Neural Networks for Software Effort Estimationcited by 3
  16. 2022Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweetscited by 3
  17. 2024Test-Time Training on Graphs with Large Language Models (LLMs)cited by 3
  18. 2021Text Guide: Improving the quality of long text classification by a text selection method based on feature importancecited by 2
  19. 2021Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotationscited by 2
  20. 2021Hierarchical information matters: Text classification via tree based graph neural networkcited by 2
  21. 2022Chinese Word Segmentation with Heterogeneous Graph Neural Networkcited by 2
  22. 2022On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processingcited by 2
  23. 2023Seq-HyGAN: Sequence Classification via Hypergraph Attention Networkcited by 2
  24. 2023Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learningcited by 2
  25. 2025Jensen-Shannon Divergence Message-Passing for Rich-Text Graph Representation Learningcited by 2
  26. 2021Joint Intent Detection and Slot Filling with Wheel-Graph Attention Networkscited by 1
  27. 2021TextRGNN: Residual Graph Neural Networks for Text Classificationcited by 1
  28. 2022Story Point Effort Estimation by Text Level Graph Neural Networkcited by 1
  29. 2022Are We Really Making Much Progress in Text Classification? A Comparative Reviewcited by 1
  30. 2022A semantic hierarchical graph neural network for text classificationcited by 1
  31. 2024MBDS: A Multi-Body Dynamics Simulation Dataset for Graph Networks Simulatorscited by 1
  32. 2024Text Classification using Graph Convolutional Networks: A Comprehensive Surveycited by 1
  33. 2025Rethinking Graph-Based Document Classification: Learning Data-Driven Structures Beyond Heuristic Approachescited by 1
  34. 2026Towards Improved Sentence Representations using Token Graphscited by 1
  35. 2021Graph Convolutional Network for Swahili News Classificationcited by 0
  36. 2022KE-QI: A Knowledge Enhanced Article Quality Identification Datasetcited by 0
  37. 2023Layout-aware Webpage Quality Assessmentcited by 0
  38. 2023Word Grounded Graph Convolutional Networkcited by 0

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