mlsanityData to 7 Oct 2026

INGREX: An Interactive Explanation Framework for Graph Neural Networks

arXiv 2211.01548 ↗First posted 3 Nov 2022cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Graph Neural Networks (GNNs) are widely used in many modern applications, necessitating explanations for their decisions. However, the complexity of GNNs makes it difficult to explain predictions. Even though several methods have been proposed lately, they can only provide simple and static explanations, which are difficult for users to understand in many scenarios. Therefore, we introduce INGREX, an interactive explanation framework for GNNs designed to aid users in comprehending model predictions. Our framework is implemented based on multiple explanation algorithms and advanced libraries. We demonstrate our framework in three scenarios covering common demands for GNN explanations to present its effectiveness and helpfulness.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 16 references that are in the map. First posted on arXiv 3 Nov 2022.

Builds on

14 of 16 references.

References · 14

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2017Billion-scale similarity search with GPUscited by 2,477
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  7. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  8. 2020Parameterized Explainer for Graph Neural Networkcited by 423
  9. 2020Explainability in Graph Neural Networks: A Taxonomic Surveycited by 383
  10. 2019InterpretML: A Unified Framework for Machine Learning Interpretabilitycited by 243
  11. 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 241
  12. 2020GCN-Based User Representation Learning for Unifying Robust Recommendation and Fraudster Detectioncited by 53
2 more
  1. 2022Toward Multiple Specialty Learners for Explaining GNNs via Online Knowledge Distillationcited by 2
  2. 2022PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processescited by 1

Built on by

Not cited yet.