Recent advancements in graph learning contributed to explaining predictions generated by Graph Neural Networks. However, existing methodologies often fall short when applied to real-world datasets. We introduce HOGE, a framework to capture higher-order structures using cell complexes, which excel at modeling higher-order relationships. In the real world, higher-order structures are ubiquitous like in molecules or social networks, thus our work significantly enhances the practical applicability of graph explanations. HOGE produces clearer and more accurate explanations compared to prior methods. Our method can be integrated with all existing graph explainers, ensuring seamless integration into current frameworks. We evaluate on GraphXAI benchmark datasets, HOGE achieves improved or comparable performance with minimal computational overhead. Ablation studies show that the performance gain observed can be attributed to the higher-order structures that come from introducing cell complexes.

翻译：图学习领域的最新进展促进了图神经网络预测解释的发展。然而，现有方法在应用于现实世界数据集时往往存在不足。我们提出了HOGE框架，该框架利用胞腔复形捕获高阶结构，其擅长建模高阶关系。在现实世界中，高阶结构普遍存在，例如在分子或社交网络中，因此我们的工作显著增强了图解释的实际适用性。与先前方法相比，HOGE能生成更清晰、更准确的解释。我们的方法可与所有现有图解释器集成，确保无缝融入当前框架。我们在GraphXAI基准数据集上进行评估，HOGE以最小的计算开销实现了改进或相当的性能。消融研究表明，观察到的性能提升可归因于引入胞腔复形所带来的高阶结构。