# Text Level Graph Neural Network for Text Classification

arXiv: https://arxiv.org/abs/1910.02356 · HTML: https://mlsanity.com/paper/1910.02356/

## Key facts

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

## Built on by (most cited first, 30 of 60)

- [Learning on Large-scale Text-attributed Graphs via Variational Inference](https://mlsanity.com/paper/2210.14709/) (2022)
- [Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction](https://mlsanity.com/paper/2111.00064/) (2021)
- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning](https://mlsanity.com/paper/2308.02565/) (2023)
- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection](https://mlsanity.com/paper/2110.07317/) (2021)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs](https://mlsanity.com/paper/2304.10668/) (2023)
- [CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection](https://mlsanity.com/paper/2204.05515/) (2022)
- [Test-Time Training for Graph Neural Networks](https://mlsanity.com/paper/2210.08813/) (2022)
- [Graph Classification by Mixture of Diverse Experts](https://mlsanity.com/paper/2103.15622/) (2021)
- [Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification](https://mlsanity.com/paper/2112.06386/) (2021)
- [InducT-GCN: Inductive Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2206.00265/) (2022)
- [Graph Neural Networks for Text Classification: A Survey](https://mlsanity.com/paper/2304.11534/) (2023)
- [Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representation](https://mlsanity.com/paper/2305.05779/) (2023)
- [Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification](https://mlsanity.com/paper/2111.00180/) (2021)
- [An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering](https://mlsanity.com/paper/2112.07191/) (2021)
- [Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias](https://mlsanity.com/paper/2309.14907/) (2023)
- [Learning to Approximate Adaptive Kernel Convolution on Graphs](https://mlsanity.com/paper/2401.11840/) (2024)
- [TextConvoNet:A Convolutional Neural Network based Architecture for Text Classification](https://mlsanity.com/paper/2203.05173/) (2022)
- [An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph](https://mlsanity.com/paper/2304.11072/) (2023)
- [Contrastive Multi-graph Learning with Neighbor Hierarchical Sifting for Semi-supervised Text Classification](https://mlsanity.com/paper/2411.16787/) (2024)
- [Understanding Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2203.16060/) (2022)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [Explicit Feature Interaction-aware Graph Neural Networks](https://mlsanity.com/paper/2204.03225/) (2022)
- [Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named Entity Recognition](https://mlsanity.com/paper/2204.05518/) (2022)
- [Heterogeneous Graph Neural Networks for Software Effort Estimation](https://mlsanity.com/paper/2206.11023/) (2022)
- [Emotion Analysis using Multi-Layered Networks for Graphical Representation of Tweets](https://mlsanity.com/paper/2207.00907/) (2022)
- [Test-Time Training on Graphs with Large Language Models (LLMs)](https://mlsanity.com/paper/2404.13571/) (2024)
- [Text Guide: Improving the quality of long text classification by a text selection method based on feature importance](https://mlsanity.com/paper/2104.07225/) (2021)

## 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.