# Graph Neural Networks for Text Classification: A Survey

arXiv: https://arxiv.org/abs/2304.11534 · HTML: https://mlsanity.com/paper/2304.11534/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 63 of its 149 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 30 of 63)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [GPT Understands, Too](https://mlsanity.com/paper/2103.10385/) (2021)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Learning Convolutional Neural Networks for Graphs](https://mlsanity.com/paper/1605.05273/) (2016)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)

## Built on by (most cited first, 15 of 15)

- [Graph Contrastive Learning via Cluster-refined Negative Sampling for Semi-supervised Text Classification](https://mlsanity.com/paper/2410.18130/) (2024)
- [Large Language Models Meet Text-Attributed Graphs: A Survey of Integration Frameworks and Applications](https://mlsanity.com/paper/2510.21131/) (2025)
- [MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection](https://mlsanity.com/paper/2508.10143/) (2025)
- [DocNet: Semantic Structure in Inductive Bias Detection Models](https://mlsanity.com/paper/2406.10965/) (2024)
- [A Structural Feature-Based Approach for Comprehensive Graph Classification](https://mlsanity.com/paper/2408.05474/) (2024)
- [PQ-GCN: Enhancing Text Graph Question Classification with Phrase Features](https://mlsanity.com/paper/2409.02481/) (2024)
- [Text2Graph: Combining Lightweight LLMs and GNNs for Efficient Text Classification in Label-Scarce Scenarios](https://mlsanity.com/paper/2512.10061/) (2025)
- [From Global to Local: Learning Context-Aware Graph Representations for Document Classification and Summarization](https://mlsanity.com/paper/2603.00021/) (2026)
- [CLiGNet: Clinical Label-Interaction Graph Network for Medical Specialty Classification from Clinical Transcriptions](https://mlsanity.com/paper/2603.22752/) (2026)
- [Robustness of Graph Self-Supervised Learning to Real-World Noise: A Case Study on Text-Driven Biomedical Graphs](https://mlsanity.com/paper/2605.05463/) (2026)
- [HyperPersona: A Multi-Level Hypergraph Framework for Text-Based Automatic Personality Prediction](https://mlsanity.com/paper/2605.17355/) (2026)
- [Invariant-Based Weight Sharing for Message Passing](https://mlsanity.com/paper/2605.25750/) (2026)
- [Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation](https://mlsanity.com/paper/2608.16269/) (2026)
- [Relational Over-Regularization: Graph-Based AI-Generated Text Detection via Sentence Transition Deviation](https://mlsanity.com/paper/2608.26694/) (2026)
- [Low resource cross-modal alignment using HGNN to enhance speech representation](https://mlsanity.com/paper/2609.23191/) (2026)

## Abstract (arXiv)

Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can be naturally cast into a graph, which captures words, documents, and corpus global features. In this survey, we bring the coverage of methods up to 2023, including corpus-level and document-level graph neural networks. We discuss each of these methods in detail, dealing with the graph construction mechanisms and the graph-based learning process. As well as the technological survey, we look at issues behind and future directions addressed in text classification using graph neural networks. We also cover datasets, evaluation metrics, and experiment design and present a summary of published performance on the publicly available benchmarks. Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey.