mlsanityData to 7 Oct 2026

LLMExplainer: Large Language Model based Bayesian Inference for Graph Explanation Generation

arXiv 2407.15351 ↗First posted 22 Jul 2024cs.LG cs.AI cs.CLcited by 8

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Abstract (arXiv)

Recent studies seek to provide Graph Neural Network (GNN) interpretability via multiple unsupervised learning models. Due to the scarcity of datasets, current methods easily suffer from learning bias. To solve this problem, we embed a Large Language Model (LLM) as knowledge into the GNN explanation network to avoid the learning bias problem. We inject LLM as a Bayesian Inference (BI) module to mitigate learning bias. The efficacy of the BI module has been proven both theoretically and experimentally. We conduct experiments on both synthetic and real-world datasets. The innovation of our work lies in two parts: 1. We provide a novel view of the possibility of an LLM functioning as a Bayesian inference to improve the performance of existing algorithms; 2. We are the first to discuss the learning bias issues in the GNN explanation problem.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 38 of its 59 references that are in the map. First posted on arXiv 22 Jul 2024.

Builds on

38 of 59 references.

References · 38

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  6. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  7. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  8. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  9. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  10. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  11. 2023VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Taskscited by 499
  12. 2020Parameterized Explainer for Graph Neural Networkcited by 423
26 more
  1. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  2. 2021Transformers Can Do Bayesian Inferencecited by 338
  3. 2021On Explainability of Graph Neural Networks via Subgraph Explorationscited by 280
  4. 2015Diffusion-Convolutional Neural Networkscited by 268
  5. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 212
  6. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  7. 2015Adding Gradient Noise Improves Learning for Very Deep Networkscited by 144
  8. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  9. 2023Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrieverscited by 91
  10. 2019Concrete Autoencoders for Differentiable Feature Selection and Reconstructioncited by 90
  11. 2022Trustworthy Graph Neural Networks: Aspects, Methods and Trendscited by 88
  12. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  13. 2024IMPRINT: Generative Object Compositing by Learning Identity-Preserving Representationcited by 64
  14. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  15. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  16. 2024SwitchTab: Switched Autoencoders Are Effective Tabular Learnerscited by 37
  17. 2018Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answeringcited by 30
  18. 2022A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metricscited by 26
  19. 2024TnT-LLM: Text Mining at Scale with Large Language Modelscited by 26
  20. 2023Learning How to Propagate Messages in Graph Neural Networkscited by 23
  21. 2021Bayesian Transformer Language Models for Speech Recognitioncited by 22
  22. 2024Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detectioncited by 22
  23. 2023MixupExplainer: Generalizing Explanations for Graph Neural Networks with Data Augmentationcited by 18
  24. 2022Modeling Network-level Traffic Flow Transitions on Sparse Datacited by 12
  25. 2024CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulnesscited by 12
  26. 2023RegExplainer: Generating Explanations for Graph Neural Networks in Regression Taskscited by 9

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 7.

8 papers.

  1. 2025CoRe: Benchmarking LLMs Code Reasoning Capabilities through Static Analysis Taskscited by 24
  2. 2025MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Modelscited by 16
  3. 2025From Nodes to Narratives: Explaining Graph Neural Networks with LLMs and Graph Contextcited by 6
  4. 2024Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignmentcited by 5
  5. 2025Trustworthy GNNs with LLMs: A Systematic Review and Taxonomycited by 1
  6. 2025GE-Chat: A Graph Enhanced RAG Framework for Evidential Response Generation of LLMscited by 1
  7. 2025X-Node: Self-Explanation is All We Needcited by 0
  8. 2025Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Usecited by 0