EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time

no code implementations • 2 May 2024 • Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu

Understanding and explaining the predictions of Graph Neural Networks (GNNs), is crucial for enhancing their safety and trustworthiness.

Building Optimal Neural Architectures using Interpretable Knowledge

1 code implementation • 20 Mar 2024 • Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu

Neural Architecture Search is a costly practice.

Image Classification Neural Architecture Search

GOAt: Explaining Graph Neural Networks via Graph Output Attribution

1 code implementation • 26 Jan 2024 • Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu

Understanding the decision-making process of Graph Neural Networks (GNNs) is crucial to their interpretability.

Attribute Decision Making

R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning

1 code implementation • ICLR 2022 • Shengyao Lu, Bang Liu, Keith G. Mills, Shangling Jui, Di Niu

Systematicity, i. e., the ability to recombine known parts and rules to form new sequences while reasoning over relational data, is critical to machine intelligence.

Relation Relational Reasoning