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arXiv · 2509.02113

HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis

Han Chen·Hanchen Wang·Hongmei Chen·Ying Zhang·Lu Qin·Wenjie Zhang
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Abstract

The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarchical structure of software. Existing methods often oversimplify programs into single level graphs, failing to model the crucial semantic relationship between high-level functional interactions and low-level instruction logic. To bridge this gap, we introduce \dataset, the largest public hierarchical graph dataset for malware analysis, comprising over \textbf{200M} Control Flow Graphs (CFGs) nested within \textbf{595K} Function Call Graphs (FCGs). This two-level representation preserves structural semantics essential for building robust detectors resilient to code obfuscation and malware evolution. We demonstrate HiGraph's utility through a large-scale analysis that reveals distinct structural properties of benign and malicious software, establishing it as a foundational benchmark for the community. The dataset and tools are publicly available at https://higraph.org.

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Han Chen, Hanchen Wang, Hongmei Chen, Ying Zhang, Lu Qin, Wenjie Zhang. 2025-09-02. HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis. https://arxiv.org/abs/2509.02113

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