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Hi-Graph

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2013-12-01 00:00:00
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HiGraph: A Large-Scale Hierarchical Graph Dataset

Hierarchical Graph Dataset for Malware Analysis with Function Call Graphs and Control Flow Graphs

arXiv Hugging Face Dataset Size Time Period License

A comprehensive hierarchical graph-based dataset for malware analysis and detection.

Overview • Interactive Explorer • Download

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:

🔗 Launch Interactive Explorer

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.
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Paper for hzcheney/Hi-Graph