# MAGNET: A Multi-Graph Attentional Network for Code Clone Detection

arXiv: https://arxiv.org/abs/2510.24241 · HTML: https://mlsanity.com/paper/2510.24241/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 56 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning](https://mlsanity.com/paper/1801.07606/) (2018)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Order Matters: Sequence to sequence for sets](https://mlsanity.com/paper/1511.06391/) (2015)
- [Detecting Code Clones with Graph Neural Networkand Flow-Augmented Abstract Syntax Tree](https://mlsanity.com/paper/2002.08653/) (2020)
- [SourcererCC: Scaling Code Clone Detection to Big Code](https://mlsanity.com/paper/1512.06448/) (2015)
- [Modeling Functional Similarity in Source Code with Graph-Based Siamese Networks](https://mlsanity.com/paper/2011.11228/) (2020)
- [Learning Program Semantics with Code Representations: An Empirical Study](https://mlsanity.com/paper/2203.11790/) (2022)
- [CC2Vec: Combining Typed Tokens with Contrastive Learning for Effective Code Clone Detection](https://mlsanity.com/paper/2405.00428/) (2024)
- [Automatically Deriving Control-Flow Graph Generators from Operational Semantics](https://mlsanity.com/paper/2010.04918/) (2020)
- [Precise Learning of Source Code Contextual Semantics via Hierarchical Dependence Structure and Graph Attention Networks](https://mlsanity.com/paper/2111.11435/) (2021)
- [AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection](https://mlsanity.com/paper/2506.14470/) (2025)
- [Code Clone Detection via an AlphaFold-Inspired Framework](https://mlsanity.com/paper/2507.15226/) (2025)

## Abstract (arXiv)

Code clone detection is a fundamental task in software engineering that underpins refactoring, debugging, plagiarism detection, and vulnerability analysis. Existing methods often rely on singular representations such as abstract syntax trees (ASTs), control flow graphs (CFGs), and data flow graphs (DFGs), which capture only partial aspects of code semantics. Hybrid approaches have emerged, but their fusion strategies are typically handcrafted and ineffective. In this study, we propose MAGNET, a multi-graph attentional framework that jointly leverages AST, CFG, and DFG representations to capture syntactic and semantic features of source code. MAGNET integrates residual graph neural networks with node-level self-attention to learn both local and long-range dependencies, introduces a gated cross-attention mechanism for fine-grained inter-graph interactions, and employs Set2Set pooling to fuse multi-graph embeddings into unified program-level representations. Extensive experiments on BigCloneBench and Google Code Jam demonstrate that MAGNET achieves state-of-the-art performance with an overall F1 score of 96.5% and 99.2% on the two datasets, respectively. Ablation studies confirm the critical contributions of multi-graph fusion and each attentional component. Our code is available at https://github.com/ZixianReid/Multigraphmatch