Title: GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability
Venue: NeurIPS 2025
Accepted: yes
Keywords: graph neural network

graph machine learning

explainability

xai

global explanation

text-based explanation

exemplar

exemplar theory
Abstract: Graph Neural Networks (GNNs) are widely used for node classification, yet their opaque decision-making limits trust and adoption. While local explanations offer insights into individual predictions, global explanation methods—those that characterize an entire class—remain underdeveloped. Existing global explainers rely on motif discovery in small graphs, an approach that breaks down in large, real-world settings where subgraph repetition is rare, node attributes are high-dimensional, and predictions arise from complex structure-attribute interactions. We propose GnnXemplar, a novel global explainer inspired from Exemplar Theory from cognitive science. GnnXemplar identifies representative nodes in the GNN embedding space—exemplars—and explains predictions using natural language rules derived from their neighborhoods. Exemplar selection is framed as a coverage maximization problem over reverse $k$-nearest neighbors, for which we provide an efficient greedy approximation. To derive interpretable rules, we employ a self-refining prompt strategy using large language models (LLMs). Experiments across diverse benchmarks show that GnnXemplar significantly outperforms existing methods in fidelity, scalability, and human interpretability, as validated by a user study with 60 participants.


Peer reviews:
comments: SUMMARY:
This paper presented GNNXEMPLAR, a novel framework for global-concept-based GNN explanation of node classification in large graph. To achieve this, the author first accesses the GNN’s embedding space to identify exemplar nodes. Finally, to generate human-understandable explanations, they leverage LLMs to distill the defining characteristics of each exemplar and its neighborhood into concise natural language rules. As a result, this approach enables global yet instance-relevant explanations grounded in learned graph structure and semantics.

STRENGTHS AND WEAKNESSES:
**Strengths:**
- The paper provides a fresh perspective on global-concept-based GNN explanation, with sufficient theoretical analysis. It is interesting to use LLM to extract rules. 
- The method shows significant improvements in fidelity, scalability, and human interpretability across diverse benchmarks.
- The paper is well-written.

**Weaknesses:**
- GraphTrail can be compared on graph datasets with discrete node labels, and the same applies to MAGE. GNNs can transform discrete node labels into continuous embeddings. So, why can't GNNXEMPLAR be applied?

QUESTIONS:
Please refer to the above weakness section for suggestions.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-07

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript proposes a new framework for GNN explainability built on several main ideas: i) use of exemplars for global explanations (representative nodes), ii) application of an LLM to generate a disjunction of exemplar-specific rules for global explanations, and iii) using ideas from k-nearest neighbor and clustering methods to select exemplars.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n\n- S1: The idea of using exemplars for global explanations is novel and quite interesting. \n- S2: The manuscript complements the novel idea of using exemplars with several key innovations which enables a practical approach to exemplar generation including use of reverse k-means for exemplar selection, use of LLMs to generate the logical proposition for exemplar-specific rules, and the way the LLM is used to generate the Python code rather than a natural language explanation directly. Each of these ideas are novel and crucial in constructing an implementable and practical framework.\n-  S3: The manuscript is well-written and contains useful examples and intuitive explanations, e.g., the example after Definition 4 is helpful.\n- S4: The theoretical results, while straightforward to prove, novel and insightful. For instance, Theorem 2, providing a lower bound on the performance of the greedy algorithm is a useful result. \nWeaknesses:\n- W1: The definitions are somewhat vague. Specifically, Definitions 3 and 4 are high level descriptions of an idea rather than concrete definitions. \n- W2: It would be helpful to comment on how the results of Theorem 2 would change if an estimate of Rev-k-NN is used (as in practical implementations here) instead of the actual Rev-k-NN.\n\nQUESTIONS:\n- Q1: [W1] The definitions are somewhat vague. Specifically, Definitions 3 and 4 are high level descriptions of an idea rather than concrete definitions. Can you provide more concrete definitions for the exemplar and signature? \n- Q2: [W2] It would be helpful to comment on how the results of Theorem 2 would change if an estimate of Rev-k-NN is used (as in practical implementations here) instead of the actual Rev-k-NN. \n- Q3: I wonder if a comparison with instance-level baselines such as GNNExplainer and PGExplainer on synthetic datasets such as BA Shapes can be provided as follows. Since the aforementioned explainers are instance-level, they need to be transformed into global explainers. Can they be combined with the LLM idea in this work, such that the explanation outputs of GNNExplainer and PGExplainer are input to the LLM to output the global rule? If so, how would the accuracy compare with the proposed explainer?\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes GNNXEMPLAR, a novel global explanation framework for Graph Neural Networks (GNNs) applied to node classification tasks. The method draws inspiration from exemplar theory in cognitive science, selecting representative nodes (exemplars) in the GNN embedding space and generating global explanations via natural language rules derived from their neighborhoods. Exemplar selection is formulated as a coverage maximization problem using reverse k-nearest neighbors, solved via a greedy algorithm with submodular approximation guarantees. The natural language rule generation employs a self-refining prompting strategy leveraging large language models (LLMs). Extensive experiments across multiple homophilous and heterophilous benchmarks demonstrate GNNXEMPLAR's superiority in fidelity, scalability, and human interpretability.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper addresses a clear gap in GNN interpretability research by focusing on global explanations for node classification on large, real-world graphs with high-dimensional node attributes. The shift from motif-based to exemplar-based explanations is both novel and well-motivated.\n2. The combination of reverse k-NN for exemplar selection and LLM-driven self-refinement for natural language rule discovery is highly innovative. The theoretical formulation, including NP-hardness proofs and submodularity-based greedy optimization, demonstrates methodological rigor.\n3. The experiments are extensive, covering a diverse set of datasets, GNN architectures, and both homophilous and heterophilous graphs. Ablation studies clearly demonstrate the contribution of each module.\n\nWeaknesses:\n1. The explanation of GNNXEMPLAR is actually \"mimicking the output behavior of GNN\", but it does not really restore the decision logic of the internal mechanism of GNN. And the whole method is highly dependent on whether GNN has learned embeddings with semantic distinction ability during training. Once the GNN training effect is poor or overfitting or underfitting, the quality of Exemplar selection and explanation rules will be affected.\n2. The comparison focuses primarily on global explainers adapted from graph classification; comparisons to more recent feature-attribution-based or counterfactual node explainers could provide additional perspective（GOAt，COMRECGC）.\n- [1] Lu S, Mills K G, He J, et al. GOAt: Explaining graph neural networks via graph output attribution[J]. arXiv preprint arXiv:2401.14578, 2024.\n- [2] Fournier G, Medya S. COMRECGC: Global Graph Counterfactual Explainer through Common Recourse[J]. arXiv preprint arXiv:2505.07081, 2025.\n3. The current framework operates only on the embedding space and lacks full visibility into the internal feature-topology interactions of GNNs, limiting its mechanistic interpretability.\n\nQUESTIONS:\nSee above weaknesses.\n\nLIMITATIONS:\nYES", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces an approach for model-level explanations of GNN\npredictions focused on node classification in large graphs, where\nmotif-based explanations are less likely to be effective (and are\nexpensive to compute). The approach combines the identification of\nexemplars node characterizing the model behavour for each class with\nthe extraction of textual explanations via LLM prompting.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n- The approach addresses a poorly explored task, which is model-level\nexplanation in node classification. \n\n- The proposed solution is sensible, well-motivated and explained. \n\n\n# Weaknesses\n\n- The problem of identifying exemplars sound very similar to the one of\ncoreset selection. It would be useful to understand why the authors\ncould not use this well-developed framework and how the proposed\nsolution differs from it. \n\n- The approach sounds like a prototype-based explainability method. I\nguess the PAGE approach [1] is a very relevant piece of work that\nshould be cited and compared with (even if the focus on graph-level\nexplainability remains).\n\n- The key element allowing LLMs to generate interpretable explanations\nis the summary of the node neighborhood. A very strong bias is\nintroduced there, as the topology is flattened into sets of l-hop\nneighbors, and the attributes are treated independently. This is\nreflected in the explanations being generated. I have the impression\nthat this approach would have problems when topology and attribute\nrelationships play a stronger role in the prediction. \n\n# Minor points\n\n- For the sake of consistency, I would use lowercase non-calligraphic letters for individual classes (calligraphic letters indicate sets in the paper)\n\n- Phi is used in Def. 4 before being introduced.\n\n[1] Shin, Y.-M., Kim, S.-W., Yoon, E.-B., & Shin, W.-Y. (2022). Prototype-Based Explanations for Graph Neural Networks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13047-13048. https://doi.org/10.1609/aaai.v36i11.21660\n\nQUESTIONS:\n- How does the reverse k-NN selection problem differs from coreset identification?\n\n- Can you clarify what precision/recall/f1 represent? are these referring to the classification performance on the explainer? what about the one of the original GNN?\n\n- BAShapes: how are labels incorporated? as additional attributes?\n\nLIMITATIONS:\nThe work does include a paragraph on limitations. However I see a main limitation that is not discussed, with is the strong bias in guiding the LLM to extract explanations, and the difficulty in\nadapting it for truly topological patterns.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}