Title: Towards Robust Heterogeneous Graph Explanations under Structural Perturbations
Venue: NeurIPS 2025
Accepted: no
Keywords: Graph Neural Network

Heterogeneous Graph

Explainability
Abstract: Explaining the prediction process of Graph Neural Networks (GNNs) is critical for enhancing model transparency and trustworthiness. However, real-world graphs are predominantly heterogeneous and often suffer from structural noise, which severely hampers the reliability of existing explanation methods. To address this challenge, we propose RoHeX, a Robust Heterogeneous Graph Neural Network Explainer. RoHeX begins with a theoretical analysis of how different heterogeneous GNN architectures amplify noise through message passing. To mitigate this effect, we introduce a denoising variational inference framework that operates on the graph structure to extract robust latent representations. Furthermore, RoHeX incorporates heterogeneous edge semantics into the subgraph generation process and formulates explanation as an optimization problem under the graph information bottleneck principle. This enables RoHeX to generate explanations that are both semantically meaningful and structurally robust. Extensive experiments on multiple real-world heterogeneous graph datasets demonstrate that RoHeX significantly outperforms state-of-the-art baselines in terms of explanation quality and robustness to noise.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript introduces an explanation mechanism for graph neural networks operating over heterogenous graphs. It is claimed that the proposed method mitigates the 'noise amplification effect' in heterogeneous GNN architectures.\n\nSTRENGTHS AND WEAKNESSES:\nThe manuscript contains numerous inaccuracies and lacks clarity in several key areas. For instance, \n> - in the proof of Theorem 3.1, the node degrees are assumed to be uniform (\"Assuming the degree distribution of neighbors is uniform\"), however, this is not stated in the theorem statement. \n>- It is unclear how equation (23) follows from (22) and how the latter follow from (21), e.g., what approximation is used.  \n>- It appears that the probabilities in Equation (3) can be greater than 1 (e.g, assume a star graph, where the average degree is close to 1 but the center has a large degree, so that $d_i/\\overline{d}\\gg 1$.\n>- It is unclear why the quantities in Equation (4) are defined in this way. No insight or intuition is provided. Furthermore $\\mathcal{E}_r$ is not formally defined. \n>- The notation in Line 62 in Section 2.1 is not defined and is unclear. \n>- It is unclear how B^+ and B^- are chosen in Equation (5)\n>- It is not clear why the authors claim that \"our method simulates real-world noise more realistically and preserves the structural properties of the original graph.\" No empirical or theoretical evidence is provided.\n>- Influence is not defined in Theorem 3.2.\n>- The statement \"this factor is significantly greater than 1.\" in Theorem 3.2 is inaccurate. The quantity is greater than 1 but it can be arbitrarily close to 1. \n>- In Section 4.1, given that the MAE and RMSE are defined with respect to indicator functions which take values {0,1}, we have MAE=RMSE^2. Consequently, they would convey the same information and do not need to be computed in Table 1 separately. Furthermore, if the indicator function notation is used correctly, then MAE and RMSE would take values between 0 and 1 since subtracting the two indicator functions would yield a value in {-1,1,0}. So, it is unclear why there are values above 1 in the table. It may be that the authors have used logit values instead of the indicator values. This needs to be clarified. \n-\n\nQUESTIONS:\nPlease see the weaknesses above.\n\nLIMITATIONS:\nPlease see the weaknesses above.", "TITLE": "Official Review", "RECOMMENDATION": 1, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 1, "ORIGINALITY": 2, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses explanation methods for heterogeneous graph neural networks (HGNNs). It first provides a theoretical analysis showing that meta-path-based HGNNs can significantly amplify noise due to the propagation of spurious meta-paths created by noisy edges. To address this issue, the authors propose a denoising variational inference framework. Unlike standard VGAE, this approach assumes that the observed graph is a noisy version of an underlying standard graph, enabling the model to learn robust node representations. The method incorporates a Gaussian Mixture Model (GMM) as the posterior distribution to better capture the complex semantics of heterogeneous graphs. Based on this inference model, the method identifies explanatory subgraphs of the GNN under the Graph Information Bottleneck (GIB) principle. Evaluation on real-world graph datasets demonstrates that the proposed approach outperforms existing GNN explanation methods regarding both fidelity and sparsity. Moreover, an ablation study confirms the effectiveness of both the denoising variational inference and the attention mechanism that captures heterogeneous relations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nThe proposed denoising variational inference, which assumes an underlying standard graph, explicitly models the presence of noise in the observed graph. This formulation has strong potential to advance robust probabilistic generative models for graph data. Furthermore, the use of a Gaussian Mixture Model (GMM) as the posterior distribution enhances the robustness and interpretability of the GNN explanations. The method demonstrates clear empirical superiority over conventional GNN explanation techniques on real-world datasets. The proposal is theoretically sound, and the derivation is clearly and accessibly presented for the reader.\n\nWeaknesses:\n\nWhile the proposed method is theoretically well-grounded and makes a significant contribution to the field, there are some concerns regarding the scope of the evaluation. Specifically, the experiments are limited to heterogeneous graphs, even though the denoising variational inference framework appears to be directly applicable to homogeneous graphs as well. It would be valuable to include evaluation on homogeneous graph datasets, where the method could be compared against existing explainers, such as GNNExplainer, which are explicitly designed for homogeneous settings. Such an evaluation would help clarify the respective contributions of the denoising inference mechanism and the attention mechanism tailored for heterogeneous relations.\n\nIn addition, there appear to be possible inconsistencies in the mathematical expressions. For instance, in Equation (8), the term $q_{\\psi}(\\mathcal{G} \\mid \\tilde{\\mathcal{G}})$ may be a typographical error and should likely be $q_{\\psi}(Z \\mid \\tilde{\\mathcal{G}})$. Similarly, in the fourth line of Equation (38) in the Appendix, the expression $q_{\\psi}(Z \\mid \\mathcal{G})$ might be intended as $q_{\\psi}(Z \\mid \\tilde{\\mathcal{G}})$, to maintain consistency with the KL divergence in the fifth line.\n\nQUESTIONS:\n1. Would it be possible to discuss how the denoising variational inference and the attention mechanism that captures heterogeneous relations, respectively, contribute to the overall performance, for example, based on evaluation experiments using homogeneous graphs?\n\n2. Regarding the potential notation errors mentioned in the Weakness section, could you clarify whether these are indeed mistakes or if I may have misunderstood them?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

comments: SUMMARY:
This paper proposes a GNN explanation method, RoHeX, which can generate robust explanations under structural noise. This paper analyzes the effects of noise during GNN message passing process, and quantitatively calculates the amplification of noise. Then RoHeX uses denoising variational inference to reduce the influence of structural noise and generates explanation subgraph. Experiments show the effectiveness of RoHeX.

STRENGTHS AND WEAKNESSES:
S1: The author theoretically proves Theorems 3.1 and 3.2 of the article with rigorous proofs.
S2: The paper's experiments are comprehensive and analyze the robustness of different modules to noise.
S3: Analyzing the impact of noise on GNN explainers is a novel direction。

W1: This paper focuses on node classification task, but lacks support for more tasks such as link prediction and graph classification tasks.
W2: The important edge sampling process can be said to be more specific. Whether it is based on threshold sampling or related type sampling or other methods needs to be explained.
W3: The noise studied by the author is mainly for the structure, and it can be expanded to the features as limitation said.

QUESTIONS:
Q1: Can RoHeX be applied to other GNN tasks like link prediction or graph classification? If yes, how does the explanation module work?
Q2: How does the structural perturbation method proposed in Section 3.1 simulate real-world structural noise?
Q3: In the explanation method in Section 3.4, how are important subgraph edges selected?

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper considers the problem of explaining predictions made by Graph Neural Networks (GNNs), particularly in the context of heterogeneous graphs that are common in real-world applications such as knowledge graphs, recommendation systems, and biological networks. These graphs contain multiple types of nodes and edges, which introduce significant semantic complexity. Moreover, such data often contains structural noise, including irrelevant or missing connections, which poses a severe challenge for existing explanation methods. To overcome these challenges, the authors propose RoHeX, a robust GNN explainer that integrates (1) a theoretical analysis of noise amplification in heterogeneous GNNs, (2) a denoising variational inference framework to learn noise-resistant latent representations, and (3) a relation-aware explanation generator guided by the Graph Information Bottleneck principle. Extensive experiments demonstrate that RoHeX outperforms existing explainers in terms of both fidelity and robustness across multiple real-world datasets. \n\nThe topic is timely and relevant, especially given the growing interest in GNN explainability under real-world conditions. The paper is generally well-written and easy to follow, with a clear motivation and reasonable experimental design. The integration of denoising and heterogeneous modeling is conceptually interesting and aligns well with practical challenges in noisy, multi-relational graphs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.The paper addresses a timely and practically important problem in GNN explainability under structural noise and graph heterogeneity.\n\n2.The proposed framework, RoHeX, integrates multiple well-motivated components, including denoising variational inference and relation-aware explanation generation.\n\n3.The writing is generally clear and accessible, with well-structured arguments and motivation.\n\nWeaknesses:\n\n1.The paper does not evaluate its explanation framework using heterogeneous graph GNN explainability metrics proposed in the xPath method, such as Accuracy Fidelity and Probability Fidelity, which is a closely related prior work focusing on heterogeneous graph explanations. Including these metrics would provide a more direct comparison with existing heterogeneous GNN explainers.\n\n2.The experimental evaluation is limited to a few datasets and does not include commonly used heterogeneous graph benchmarks, such as IMDB. Incorporating such datasets would strengthen the empirical validation and demonstrate broader applicability.\n\n3.The experimental evaluation does not include commonly used graph benchmarks, such as PROTEINS and DD. It would be helpful to understand how the method performs on simpler graph benchmarks (e.g., non-heterogeneous graphs), as this could provide further insight into its generalizability across different graph settings.\n\nQUESTIONS:\n1.The paper does not use explanation metrics proposed in xPath, such as Accuracy Fidelity and Probability Fidelity. Are these metrics incompatible with RoHeX, or was there a specific reason for not including them in the evaluation?\n\n2.The experiments do not include commonly used heterogeneous graph benchmarks such as IMDB. Has RoHeX been tested on these datasets? If so, how does it perform in those settings?\n\n3.Could the authors comment on how RoHeX would perform on simpler, non-heterogeneous graph benchmarks? Including such results may offer additional insights into the generalizability and robustness of the method across different graph structures.\n\nLIMITATIONS:\nThere is no obvious potential negative societal impact for this paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}