提出新框架提升图神经网络解释性，兼顾效率与结构洞察。
Enhancing Explainability of Graph Neural Networks Through Conceptual and Structural Analyses and Their Extensions
- 融合概念与结构分析，动态解析图神经网络决策路径。
- 相比传统方法，计算开销更低且不依赖模型内部细节。
- 适合需要可解释性的图学习应用，如医疗、金融风控。
图神经网络（GNNs）已成为建模和分析图结构数据的强大工具，在众多应用中得到广泛采用。然而，这些方法的复杂性常常阻碍对其决策过程的理解。当前可解释人工智能（XAI）方法难以理清图中复杂的关联与交互。尽管已有研究尝试通过后处理或自解释设计来弥补这一差距，但多数方法仍局限于图结构分析，仅关注与预测结果相关的特征模式。后处理方法虽灵活但需额外计算资源，且因无法访问模型内部机制而可靠性较低；而可解释模型虽能即时提供解释，却在不同场景下的泛化能力存疑。为此，本文提出一种专为图机器学习设计的新XAI框架，旨在实现适应性强、计算高效且超越单个特征分析的解释能力，深入揭示图结构如何影响预测结果。
原文摘要 · Abstract (English)
Graph Neural Networks (GNNs) have become a powerful tool for modeling and analyzing data with graph structures. The wide adoption in numerous applications underscores the value of these models. However, the complexity of these methods often impedes understanding their decision-making processes. Current Explainable AI (XAI) methods struggle to untangle the intricate relationships and interactions within graphs. Several methods have tried to bridge this gap via a post-hoc approach or self-interpretable design. Most of them focus on graph structure analysis to determine essential patterns that correlate with prediction outcomes. While post-hoc explanation methods are adaptable, they require extra computational resources and may be less reliable due to limited access to the model's internal workings. Conversely, Interpretable models can provide immediate explanations, but their generalizability to different scenarios remains a major concern. To address these shortcomings, this thesis seeks to develop a novel XAI framework tailored for graph-based machine learning. The proposed framework aims to offer adaptable, computationally efficient explanations for GNNs, moving beyond individual feature analysis to capture how graph structure influences predictions.
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