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insight - Geometric Graph Neural Networks
A Comprehensive Survey of Geometric Graph Neural Networks: Data Structures, Models, and Applications
Geometric graph neural networks are essential for modeling scientific problems with geometric features, addressing the limitations of traditional GNNs.
Analyzing the Expressive Power of Geometric Graph Neural Networks
The author explores the expressive power of geometric GNNs through the Geometric Weisfeiler-Leman (GWL) framework, highlighting key design choices that influence their expressivity.
Geometric GNNs for 3D Atomic Systems: A Comprehensive Overview
物理的対称性を尊重した3D原子系の幾何学GNNの包括的な概要
Geometric Graph Neural Networks: Data Structures, Models, and Applications
Geometric Graph Neural Networks aim to model scientific problems with geometric features, utilizing invariant/equivariant properties to enhance the understanding of geometric graphs.
Geometric Graph Neural Networks: Expressive Power and Discrimination
Geometric Graph Neural Networks' expressive power and discrimination capabilities are crucial for understanding their design choices and practical implications.
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