# Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

arXiv: https://arxiv.org/abs/2004.13826 · HTML: https://mlsanity.com/paper/2004.13826/

## Key facts

- Cited by 77 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 30 of 77)

- [Retrieval-Augmented Generation with Graphs (GraphRAG)](https://mlsanity.com/paper/2501.00309/) (2024)
- [Learning on Large-scale Text-attributed Graphs via Variational Inference](https://mlsanity.com/paper/2210.14709/) (2022)
- [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)
- [Dynamic Graph Collaborative Filtering](https://mlsanity.com/paper/2101.02844/) (2021)
- [ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection](https://mlsanity.com/paper/2110.07317/) (2021)
- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2201.06885/) (2022)
- [Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices](https://mlsanity.com/paper/2111.03220/) (2021)
- [MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms](https://mlsanity.com/paper/2209.06800/) (2022)
- [Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models](https://mlsanity.com/paper/2308.11578/) (2023)
- [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)
- [Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification](https://mlsanity.com/paper/2111.00180/) (2021)
- [Cross-lingual Text Classification with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2105.11246/) (2021)
- [Transformers are Short Text Classifiers: A Study of Inductive Short Text Classifiers on Benchmarks and Real-world Datasets](https://mlsanity.com/paper/2211.16878/) (2022)
- [Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks](https://mlsanity.com/paper/2102.11479/) (2021)
- [Relation-aware Heterogeneous Graph for User Profiling](https://mlsanity.com/paper/2110.07181/) (2021)
- [Generic and Trend-aware Curriculum Learning for Relation Extraction in Graph Neural Networks](https://mlsanity.com/paper/2205.08625/) (2022)
- [Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification](https://mlsanity.com/paper/2410.18130/) (2024)
- [FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition](https://mlsanity.com/paper/2208.11464/) (2022)
- [Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks](https://mlsanity.com/paper/2210.05498/) (2022)
- [Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification](https://mlsanity.com/paper/2302.08671/) (2023)
- [Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs](https://mlsanity.com/paper/2505.14699/) (2025)
- [State of the Art and Potentialities of Graph-level Learning](https://mlsanity.com/paper/2301.05860/) (2023)
- [DocGraphLM: Documental Graph Language Model for Information Extraction](https://mlsanity.com/paper/2401.02823/) (2024)
- [Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution](https://mlsanity.com/paper/2201.02510/) (2021)
- [An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph](https://mlsanity.com/paper/2304.11072/) (2023)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)

## Abstract (arXiv)

Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task. However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words. In this work, to overcome such problems, we propose TextING for inductive text classification via GNN. We first build individual graphs for each document and then use GNN to learn the fine-grained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document. Finally, the word nodes are aggregated as the document embedding. Extensive experiments on four benchmark datasets show that our method outperforms state-of-the-art text classification methods.