# Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification

arXiv: https://arxiv.org/abs/2512.20872 · HTML: https://mlsanity.com/paper/2512.20872/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Recipe for a General, Powerful, Scalable Graph Transformer](https://mlsanity.com/paper/2205.12454/) (2022)
- [Exphormer: Sparse Transformers for Graphs](https://mlsanity.com/paper/2303.06147/) (2023)
- [A Large-Scale Database for Graph Representation Learning](https://mlsanity.com/paper/2011.07682/) (2020)
- [DL-Droid: Deep learning based android malware detection using real devices](https://mlsanity.com/paper/1911.10113/) (2019)
- [Graph Neural Network-based Android Malware Classification with Jumping Knowledge](https://mlsanity.com/paper/2201.07537/) (2022)
- [Learning Large Graph Property Prediction via Graph Segment Training](https://mlsanity.com/paper/2305.12322/) (2023)
- [DeepCatra: Learning Flow- and Graph-based Behaviors for Android Malware Detection](https://mlsanity.com/paper/2201.12876/) (2022)
- [Android Malware Detection using Large-scale Network Representation Learning](https://mlsanity.com/paper/1806.04847/) (2018)
- [Tarallo: Evading Behavioral Malware Detectors in the Problem Space](https://mlsanity.com/paper/2506.02660/) (2025)
- [IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense](https://mlsanity.com/paper/2512.20004/) (2025)

## Abstract (arXiv)

Function call graphs (FCGs) have emerged as a powerful abstraction for malware detection, capturing the behavioral structure of applications beyond surface-level signatures. Their utility in traditional program analysis has been well established, enabling effective classification and analysis of malicious software. In the mobile domain, especially in the Android ecosystem, FCG-based malware classification is particularly critical due to the platform's widespread adoption and the complex, component-based structure of Android apps. However, progress in this direction is hindered by the lack of large-scale, high-quality Android-specific FCG datasets. Existing datasets are often outdated, dominated by small or redundant graphs resulting from app repackaging, and fail to reflect the diversity of real-world malware. These limitations lead to overfitting and unreliable evaluation of graph-based classification methods. To address this gap, we introduce Better Call Graphs (BCG), a comprehensive dataset of large and unique FCGs extracted from recent Android application packages (APKs). BCG includes both benign and malicious samples spanning various families and types, along with graph-level features for each APK. Through extensive experiments using baseline classifiers, we demonstrate the necessity and value of BCG compared to existing datasets. BCG is publicly available at https://erdemub.github.io/BCG-dataset.