HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis
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Abstract (arXiv)
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 200M Control Flow Graphs (CFGs) nested within 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 2 Sep 2025.
Builds on
18 of 39 references.
References · 18
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6 more
- 2020A Large-Scale Database for Graph Representation Learningcited by 54
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Citing papers per year: 2026: 1.
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