Towards panoramic explainability of graph neural networks
Abstract
HKUST Electronic Theses Towards panoramic explainability of graph neural networks by Ge Lv thesis 2023 1 online resource (xiii, 103 pages) : illustrations (some color) Graph Neural Networks (GNNs) have demonstrated outstanding effectiveness on both heterogeneous and homogeneous graphs. However,
The abstract as provided by the source is truncated; see the original source.
Keywords
Not available
Specialisations
Similar theses
- The expressivity and explainability of graph neural networks Matthew Morris · University of Oxford · 2026
- One-Class Learning for Graph Neural Networks Marcos Paulo Silva Gôlo · Universidade de São Paulo · 2026
- Causal Inference Explanations for Graph Neural Networks Sahil Satish Kumar · 2025
- Network anomaly detection with graph neural networks Hamid Latif-Martínez · Universitat Politècnica de Catalunya · 2025
- Explaining Graph Neural Networks Tobias Christian Nauen · Leibniz Universität Hannover · 2021
- Scalability and interpretability of graph neural networks for small molecules Manjot. Sangha · Massachusetts Institute of Technology · 2018
