Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks

• URL: http://arxiv.org/abs/2506.00437v1
• Date: Sat, 31 May 2025 07:34:54 GMT
• Title: Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
• Authors: Jiaxing Zhang, Xiaoou Liu, Dongsheng Luo, Hua Wei,
• Abstract summary: We introduce an explainer framework with the confidence scoring module ( ConfExplainer) that quantifies the reliability of generated explanations.<n> Experimental results demonstrate the superiority of our approach, highlighting the effectiveness of the confidence score in enhancing the robustness and trustworthiness of GNN explanations.
• Score: 10.968491611749323
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Explaining Graph Neural Networks (GNNs) has garnered significant attention due to the need for interpretability, enabling users to understand the behavior of these black-box models better and extract valuable insights from their predictions. While numerous post-hoc instance-level explanation methods have been proposed to interpret GNN predictions, the reliability of these explanations remains uncertain, particularly in the out-of-distribution or unknown test datasets. In this paper, we address this challenge by introducing an explainer framework with the confidence scoring module ( ConfExplainer), grounded in theoretical principle, which is generalized graph information bottleneck with confidence constraint (GIB-CC), that quantifies the reliability of generated explanations. Experimental results demonstrate the superiority of our approach, highlighting the effectiveness of the confidence score in enhancing the trustworthiness and robustness of GNN explanations.

Related papers

• Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approach [55.43914153271912]
  Graph neural networks (GNNs) excel in graph representation learning by integrating graph structure and node features.<n>Existing GNNs fail to account for the uncertainty of class probabilities that vary with the depth of the model, leading to unreliable and risky predictions in real-world scenarios.<n>We propose a novel Evidence Fusing Graph Neural Network (EFGNN for short) to achieve trustworthy prediction, enhance node classification accuracy, and make explicit the risk of wrong predictions.
  arXiv  Detail & Related papers  (2025-06-16T03:59:38Z)
• Uncertainty in Graph Neural Networks: A Survey [47.785948021510535]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications.<n>However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions.<n>This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction [34.87646720253128]
  We propose a novel uncertainty modeling framework for self-explaining neural networks. We show it provides strong distribution-free uncertainty modeling performance for the generated explanations. It also excels in producing efficient and effective prediction sets for the final predictions.
  arXiv  Detail & Related papers  (2024-01-03T05:51:49Z)
• Factorized Explainer for Graph Neural Networks [7.382632811417645]
  Graph Neural Networks (GNNs) have received increasing attention due to their ability to learn from graph-structured data. Post-hoc instance-level explanation methods have been proposed to understand GNN predictions. We introduce a novel factorized explanation model with theoretical performance guarantees.
  arXiv  Detail & Related papers  (2023-12-09T15:29:45Z)
• ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks [7.077341403454516]
  Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and enable reliable decision-making, many GNN explainers have been proposed in recent years. We introduce Auxiliary Generative Adrative Network (ACGAN) into the field of GNN explanation and propose a new GNN explainer dubbedemphACGANGNNExplainer.
  arXiv  Detail & Related papers  (2023-09-29T01:20:28Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature [63.78619502896071]
  Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. We propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs. Specifically, TrustGNN designs specific propagative patterns for different propagative processes of trust, and distinguishes the contribution of different propagative processes to create new trust.
  arXiv  Detail & Related papers  (2022-05-25T13:57:03Z)
• Deconfounding to Explanation Evaluation in Graph Neural Networks [136.73451468551656]
  We argue that a distribution shift exists between the full graph and the subgraph, causing the out-of-distribution problem. We propose Deconfounded Subgraph Evaluation (DSE) which assesses the causal effect of an explanatory subgraph on the model prediction.
  arXiv  Detail & Related papers  (2022-01-21T18:05:00Z)
• Towards the Explanation of Graph Neural Networks in Digital Pathology with Information Flows [67.23405590815602]
  Graph Neural Networks (GNNs) are widely adopted in digital pathology. Existing explainers discover an explanatory subgraph relevant to the prediction. An explanatory subgraph should be not only necessary for prediction, but also sufficient to uncover the most predictive regions. We propose IFEXPLAINER, which generates a necessary and sufficient explanation for GNNs.
  arXiv  Detail & Related papers  (2021-12-18T10:19:01Z)
• SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods [0.0]
  We propose a method to improve the explanation quality of node classification tasks through aggregation of auxiliary explanations. Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques. Experiments on matching motif-participating nodes from a given graph show great improvement in explanation accuracy of up to 12.71%.
  arXiv  Detail & Related papers  (2021-06-16T03:04:46Z)
• How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks [19.648814035399013]
  Explainable AI (XAI) aims to provide interpretations for predictions made by learning machines, such as deep neural networks. We propose a new framework that allows to convert any arbitrary explanation method for neural networks into an explanation method for Bayesian neural networks. We demonstrate the effectiveness and usefulness of our approach extensively in various experiments.
  arXiv  Detail & Related papers  (2020-06-16T08:54:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.