# ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

arXiv: https://arxiv.org/abs/2110.07317 · HTML: https://mlsanity.com/paper/2110.07317/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 14 Oct 2021.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [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)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](https://mlsanity.com/paper/1909.03496/) (2019)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [Towards Sparse Hierarchical Graph Classifiers](https://mlsanity.com/paper/1811.01287/) (2018)
- [Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks](https://mlsanity.com/paper/2004.13826/) (2020)
- [Text Level Graph Neural Network for Text Classification](https://mlsanity.com/paper/1910.02356/) (2019)
- [Universal Graph Transformer Self-Attention Networks](https://mlsanity.com/paper/1909.11855/) (2019)
- [Quaternion Graph Neural Networks](https://mlsanity.com/paper/2008.05089/) (2020)

## Built on by (most cited first, 30 of 32)

- [An Empirical Study of Deep Learning Models for Vulnerability Detection](https://mlsanity.com/paper/2212.08109/) (2022)
- [Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning](https://mlsanity.com/paper/2406.03718/) (2024)
- [Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection](https://mlsanity.com/paper/2212.08108/) (2022)
- [Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation](https://mlsanity.com/paper/2404.15687/) (2024)
- [Investigating Large Language Models for Code Vulnerability Detection: An Experimental Study](https://mlsanity.com/paper/2412.18260/) (2024)
- [When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection](https://mlsanity.com/paper/2308.10523/) (2023)
- [VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection](https://mlsanity.com/paper/2509.11523/) (2025)
- [Just-in-Time Detection of Silent Security Patches](https://mlsanity.com/paper/2312.01241/) (2023)
- [Use of Graph Neural Networks in Aiding Defensive Cyber Operations](https://mlsanity.com/paper/2401.05680/) (2024)
- [Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++](https://mlsanity.com/paper/2407.17053/) (2024)
- [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)
- [Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction](https://mlsanity.com/paper/2504.13676/) (2025)
- [Reducing False Positives in Static Bug Detection with LLMs: An Empirical Study in Industry](https://mlsanity.com/paper/2601.18844/) (2026)
- [XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection](https://mlsanity.com/paper/2309.14677/) (2023)
- [Statement-Level Vulnerability Detection: Learning Vulnerability Patterns Through Information Theory and Contrastive Learning](https://mlsanity.com/paper/2209.10414/) (2022)
- [On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing](https://mlsanity.com/paper/2210.03123/) (2022)
- [EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning](https://mlsanity.com/paper/2403.17980/) (2024)
- [MVD: A Multi-Lingual Software Vulnerability Detection Framework](https://mlsanity.com/paper/2412.06166/) (2024)
- [Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities](https://mlsanity.com/paper/2306.06109/) (2023)
- [Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis](https://mlsanity.com/paper/2409.00199/) (2024)
- [Direction for Detection: A Survey of Automated Vulnerability Detection and all of its Pain Points](https://mlsanity.com/paper/2412.11194/) (2024)
- [MFH: A Multi-faceted Heuristic Algorithm Selection Approach for Software Verification](https://mlsanity.com/paper/2503.22228/) (2025)
- [A Benchmark Dataset for Graph Regression with Homogeneous and Multi-Relational Variants](https://mlsanity.com/paper/2505.23875/) (2025)
- [Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches](https://mlsanity.com/paper/2506.11939/) (2025)
- [Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimization](https://mlsanity.com/paper/2508.02840/) (2025)
- [Weakly Supervised Vulnerability Localization via Multiple Instance Learning](https://mlsanity.com/paper/2509.11312/) (2025)
- [MulVuln: Enhancing Pre-trained LMs with Shared and Language-Specific Knowledge for Multilingual Vulnerability Detection](https://mlsanity.com/paper/2510.04397/) (2025)
- [Robust Smart Contract Vulnerability Detection via Contrastive Learning-Enhanced Granular-ball Training](https://mlsanity.com/paper/2603.27734/) (2026)
- [Software Vulnerability Detection Using a Lightweight Graph Neural Network](https://mlsanity.com/paper/2603.29216/) (2026)

## Abstract (arXiv)

Identifying vulnerabilities in the source code is essential to protect the software systems from cyber security attacks. It, however, is also a challenging step that requires specialized expertise in security and code representation. To this end, we aim to develop a general, practical, and programming language-independent model capable of running on various source codes and libraries without difficulty. Therefore, we consider vulnerability detection as an inductive text classification problem and propose ReGVD, a simple yet effective graph neural network-based model for the problem. In particular, ReGVD views each raw source code as a flat sequence of tokens to build a graph, wherein node features are initialized by only the token embedding layer of a pre-trained programming language (PL) model. ReGVD then leverages residual connection among GNN layers and examines a mixture of graph-level sum and max poolings to return a graph embedding for the source code. ReGVD outperforms the existing state-of-the-art models and obtains the highest accuracy on the real-world benchmark dataset from CodeXGLUE for vulnerability detection. Our code is available at: urlhttps://github.com/daiquocnguyen/GNN-ReGVD.