Explaining the decision-making process of Graph Neural Networks (GNNs) is essential for improving their transparency and reliability. However, real-world graphs are often heterogeneous and subject to structural noise, posing severe challenges to the robustness of existing explanation methods. To address these issues, we propose RoHeX, a Robust Heterogeneous GNN Explainer that enhances explanation quality under noisy conditions. RoHeX begins with a theoretical analysis revealing how different heterogeneous GNN architectures amplify structural perturbations through message passing. Building on this insight, we design a denoising variational inference framework that filters noisy structures and learns robust latent graph representations. Furthermore, we incorporate relation-aware heterogeneous semantics into the explanation generation process, formulating explanation as an optimization problem under the graph information bottleneck principle. This formulation enables RoHeX to balance fidelity and compactness, producing explanations that are both semantically meaningful and structurally stable. Comprehensive experiments on multiple real-world heterogeneous graphs demonstrate that RoHeX consistently surpasses state-of-the-art baselines in explanation fidelity, robustness to structural perturbations, and explainability.

Spectral Graph Neural Networks effectively handle graphs with different homophily levels, with low-pass filter mining feature smoothness and high-pass filter capturing differences. When these distinct filters could naturally form two opposite views for self-supervised learning, the commonalities between these counterparts for the same node remain unexplored, leading to suboptimal performance. In this paper, a simple yet effective self-supervised contrastive framework, LOHA, is proposed to address this gap. LOHA optimally leverages low-pass and high-pass views by embracing "harmony in diversity". Rather than solely maximizing the difference between these distinct views, which may lead to feature separation, LOHA harmonizes the diversity by treating the propagation of graph signals from both views as a composite feature. Specifically, a novel high-dimensional feature named spectral signal trend is proposed to serve as the basis for the composite feature, which remains relatively unaffected by changing filters and focuses solely on original feature differences. LOHA achieves an average performance improvement of 2.8% over runner-up models on 9 real-world datasets with varying homophily levels. Notably, LOHA even surpasses fully-supervised models on several datasets, which underscores the potential of LOHA in advancing the efficacy of spectral GNNs for diverse graph structures.

Current graph topology augmentation methods are mostly static and heavily rely on the assumption of homophily, where connected nodes are presumed to share the same labels by default. Due to the complexity of real-world graphs, the underlying assumption is often disrupted, thus performance declines, demonstrating their limited adaptability. Although learnable methods flexibly change augmentation strategies based on data, ignorance of balancing consistency and diversity leads to suboptimal performance. This gap highlights the need for universally applicable graph augmentation strategies that ensure these two aspects, thereby enhancing model robustness. To address these challenges, we propose the RETAIN framework as an adaptive data augmentation method for graphs with different homophily levels. This method can dynamically adjust the graph structure based on a learned conditional distribution with the aid of a graph explainer, thus balancing the consistency and diversity of the augmented data. By framing graph topology modification and model refinement as a joint optimization problem, RETAIN facilitates concurrent learning from augmented data and model optimization. Empirical evaluations across diverse benchmarks on node classification tasks reveal that RETAIN can be effectively combined with other methods in a plug-and-play manner and consistently yields performance improvement across a diverse set of benchmarks for both homophilic and heterophilic graphs.