Awesome Explainable Graph Reasoning
A curated collection of research papers and software for explainable graph machine learning and reasoning.
What is Awesome Explainable Graph Reasoning?
Awesome Explainable Graph Reasoning is a curated collection of research papers and software resources focused on explainability in graph machine learning. It addresses the challenge of interpreting complex graph-based models like graph neural networks (GNNs) by compiling literature and tools that make these systems more transparent. The repository serves as a knowledge hub for understanding how and why graph ML models make decisions.
Researchers, data scientists, and machine learning engineers working with graph data who need to interpret or explain model predictions, particularly in fields like bioinformatics, social network analysis, or recommendation systems.
It provides a centralized, organized, and community-maintained resource that saves time in literature review and tool discovery, specifically tailored to the niche of explainable graph reasoning, which is less covered in general AI explainability collections.
Overview
A collection of research papers and software related to explainability in graph machine learning.
Use Cases
Best For
- Finding survey papers on explainable graph neural networks
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