configs:
- config_name: default
data_files:
- split: train
path: data/*.parquet
HiGraph: A Large-Scale Hierarchical Graph Dataset
Hierarchical Graph Dataset for Malware Analysis with Function Call Graphs and Control Flow Graphs
A comprehensive hierarchical graph-based dataset for malware analysis and detection.
Overview
HiGraph is a novel, large-scale dataset that models each application as a hierarchical graph: a local Control Flow Graph (CFG) capturing intra-function logic and a global Function Call Graph (FCG) capturing inter-function interactions.
Graph-based methods have shown great promise in malware analysis, yet the lack of large-scale, hierarchical graph datasets limits further advances in this field. This hierarchical design facilitates the development of robust detection models that are more resilient to obfuscation, model aging, and malware evolution.
Key Features
- 🔍 Hierarchical Graph Structure: Two-level representation with FCGs and CFGs
- 📈 Large Scale: 200M+ Control Flow Graphs and 499K+ Function Call Graphs
- 🏷️ Rich Semantic Information: Preserves crucial structural details for malware analysis
- 📊 Comprehensive Coverage: 11-year temporal span (2012-2022)
- 🎯 Benchmark Ready: Designed for advancing hierarchical graph learning in cybersecurity
Interactive Visualization
Explore the hierarchical structure of malware samples through our interactive visualization tool:
Click to explore the complete dataset structure and sample graphs
Download Dataset
Access the complete HiGraph dataset through multiple platforms:
| Platform | Description | Link |
|---|---|---|
| 🤗 Hugging Face | Primary dataset repository | View on Hugging Face |
| 🌐 Project Page | Interactive explorer | HiGraph Explorer |
Citation
If you find HiGraph useful in your research, please cite:
@article{chen2025higraph,
title={HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis},
author={Chen, Han and Wang, Hanchen and Chen, Hongmei and Zhang, Ying and Qin, Lu and Zhang, Wenjie},
journal={arXiv preprint arXiv:2509.02113},
year={2025}
}
Requirements
- Python >= 3.9
- torch==2.6.0
- torch-geometric==2.6.1
Install dependencies:
pip install -r requirements.txt
📄 License
This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) license. See the LICENSE file for details.