  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.
