CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks

• URL: http://arxiv.org/abs/2606.20747v1
• Date: Wed, 17 Jun 2026 23:55:31 GMT
• Title: CIExplainer++: Generating Causal and Interpretable Explanations for Graph Neural Networks
• Abstract summary: Explainable Artificial Intelligence aims to make black-box models more trustworthy by presenting, in a human-understandable manner, the elements that lead to the model's output.<n>This involves both (i) identifying components and connections with genuine causal influence on outputs and (ii) translating such structures into an interpretable representation.<n>We introduce CIExplainer, a novel perturbation-based method grounded in causal inference for explaining Graph Neural Networks (GNNs)
• Score: 1.2366208723499545
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Explainable Artificial Intelligence aims to make black-box models more trustworthy by presenting, in a human-understandable manner, the elements that lead to the model's output. This involves both (i) identifying components and connections with genuine causal influence on outputs and (ii) translating such structures into an interpretable representation. For the former, we introduce CIExplainer, a novel perturbation-based method grounded in causal inference for explaining Graph Neural Networks (GNNs). CIExplainer identifies the subgraph with the highest causal effects on GNN predictions using the Potential Outcome Framework. We evaluate and compare CIExplainer on various GNN architectures (GCN, GraphSAGE, GAT, GIN) and datasets. To bridge subgraph explanations with human interpretability, we further propose G2TeXplainer, a method that transforms causal subgraphs into natural language explanations that capture both feature-level and relational information.

Related papers

• GraphXAIN: Narratives to Explain Graph Neural Networks [0.0]
  Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data.<n>Existing GNN explanation methods yield technical outputs that are difficult for non-data scientists to understand.<n>We propose GraphXAIN, a method that generates natural language narratives explaining GNN predictions.
  arXiv  Detail & Related papers  (2024-11-04T19:21:06Z)
• 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)
• GANExplainer: GAN-based Graph Neural Networks Explainer [5.641321839562139]
  It is critical to explain why graph neural network (GNN) makes particular predictions for them to be believed in many applications. We propose GANExplainer, based on Generative Adversarial Network (GAN) architecture. GANExplainer improves explanation accuracy by up to 35% compared to its alternatives.
  arXiv  Detail & Related papers  (2022-12-30T23:11:24Z)
• Explainability in subgraphs-enhanced Graph Neural Networks [12.526174412246107]
  Subgraphs-enhanced Graph Neural Networks (SGNNs) have been introduced to enhance the expressive power of GNNs. In this work, we adapt PGExplainer, one of the most recent explainers for GNNs, to SGNNs. We show that our framework is successful in explaining the decision process of an SGNN on graph classification tasks.
  arXiv  Detail & Related papers  (2022-09-16T13:39:10Z)
• Discovering the Representation Bottleneck of Graph Neural Networks from Multi-order Interactions [51.597480162777074]
  Graph neural networks (GNNs) rely on the message passing paradigm to propagate node features and build interactions. Recent works point out that different graph learning tasks require different ranges of interactions between nodes. We study two common graph construction methods in scientific domains, i.e., emphK-nearest neighbor (KNN) graphs and emphfully-connected (FC) graphs.
  arXiv  Detail & Related papers  (2022-05-15T11:38:14Z)
• Reinforced Causal Explainer for Graph Neural Networks [112.57265240212001]
  Explainability is crucial for probing graph neural networks (GNNs) We propose a reinforcement learning agent, Reinforced Causal Explainer (RC-Explainer) RC-Explainer generates faithful and concise explanations, and has a better power to unseen graphs.
  arXiv  Detail & Related papers  (2022-04-23T09:13:25Z)
• Towards Self-Explainable Graph Neural Network [24.18369781999988]
  Graph Neural Networks (GNNs) generalize the deep neural networks to graph-structured data. GNNs lack explainability, which limits their adoption in scenarios that demand the transparency of models. We propose a new framework which can find $K$-nearest labeled nodes for each unlabeled node to give explainable node classification.
  arXiv  Detail & Related papers  (2021-08-26T22:45:11Z)
• Jointly Attacking Graph Neural Network and its Explanations [50.231829335996814]
  Graph Neural Networks (GNNs) have boosted the performance for many graph-related tasks. Recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs. We propose a novel attack framework (GEAttack) which can attack both a GNN model and its explanations by simultaneously exploiting their vulnerabilities.
  arXiv  Detail & Related papers  (2021-08-07T07:44:33Z)
• Generative Causal Explanations for Graph Neural Networks [39.60333255875979]
  Gem is a model-agnostic approach for providing interpretable explanations for any GNNs on various graph learning tasks. It achieves a relative increase of the explanation accuracy by up to $30%$ and speeds up the explanation process by up to $110times$ as compared to its state-of-the-art alternatives.
  arXiv  Detail & Related papers  (2021-04-14T06:22:21Z)
• Parameterized Explainer for Graph Neural Network [49.79917262156429]
  We propose PGExplainer, a parameterized explainer for Graph Neural Networks (GNNs) Compared to the existing work, PGExplainer has better generalization ability and can be utilized in an inductive setting easily. Experiments on both synthetic and real-life datasets show highly competitive performance with up to 24.7% relative improvement in AUC on explaining graph classification.
  arXiv  Detail & Related papers  (2020-11-09T17:15:03Z)
• XGNN: Towards Model-Level Explanations of Graph Neural Networks [113.51160387804484]
  Graphs neural networks (GNNs) learn node features by aggregating and combining neighbor information. GNNs are mostly treated as black-boxes and lack human intelligible explanations. We propose a novel approach, known as XGNN, to interpret GNNs at the model-level.
  arXiv  Detail & Related papers  (2020-06-03T23:52:43Z)

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.