Graph Neural Networks (GNNs) have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings together six related studies that collectively address these issues. The portfolio begins with a survey of graph-based malware detection and explainability, then advances to new graph reduction methods, integrated reduction-learning approaches, and investigations into the consistency of explanations. It also introduces dual explanation techniques based on subgraph matching and develops ensemble-based models with attention-guided stacked GNNs to improve interpretability. In parallel, curated datasets of control flow graphs are released to support reproducibility and enable future research. Together, these contributions form a coherent line of research that strengthens GNN-based malware detection by enhancing efficiency, increasing transparency, and providing solid experimental foundations.

翻译：图神经网络（GNNs）通过捕捉程序执行的图结构表示，已成为恶意软件检测的有效工具。然而，在可扩展性、可解释性以及可靠数据集的可用性方面仍存在重要挑战。本文汇集了六项相关研究，共同应对这些问题。该研究组合首先综述了基于图的恶意软件检测与可解释性方法，进而推进至新的图约简方法、集成约简-学习策略以及对解释一致性的探究。同时，本文引入了基于子图匹配的双重解释技术，并开发了基于注意力引导堆叠GNN的集成模型以提升可解释性。此外，研究还发布了经过系统构建的控制流图数据集，以支持可重复性研究并为未来工作提供基础。这些成果共同构成了一条连贯的研究脉络，通过提升效率、增强透明度及夯实实验基础，强化了基于GNN的恶意软件检测体系。