Work

GraphMinNet: Learning dependencies in graphs with light complexity minimal architecture

Year: 2026

Type: article

Source: Neural Networks

DOI: 10.1016/j.neunet.2026.109501

Authors: Md Atik Ahamed, Andrew Cheng, Qiang Ye, Qiang Cheng

Institutions: University of Kentucky, University of California, Santa Barbara

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health

Awards: G3251540411, EAGER: PBI: Modeling Place-based Innovation by Leveraging AI-Enabled Dynamic Graph Techniques, G5823907790, G5934509782, Gene Profile Analysis of Associations between Alzheimer’s Disease and Circadian Rhythmic Patterns of Human Brain Regions Using Machine Learning, RI: Small: Optimal Transport Generative Adversarial Networks: Theory, Algorithms, and Applications, G7291403024, G9000011571

OpenAlex ID: W4407123392

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