GnnXemplar: Exemplars to Explanations - Natural Language Rules for Global GNN Interpretability

NeurIPSOral2025

Authors
Burouj Armgaan, Eshan Jain, Harsh Pandey, Mahesh Chandran, Sayan Ranu
Affiliation
Indian Institute of Technology Delhi
Venue
NeurIPS 2025
Track
Oral

TL;DR

We generate global text-based explanations using representative nodes (exemplars) in the embedding space. The exemplars are selected via coverage maximization, and their signatures are explained using natural language rules from a self-refining LLM.

Opening excerpt from the authors’ abstract. source

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Topics

interpretability llm rag

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