スパース幾何MPNNの表現能力について：連結性と剛性の影響
スパース幾何グラフにおけるメッセージパッシングニューラルネットワーク（MPNN）の表現能力は、基礎となるグラフの連結性と剛性に依存する。
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.
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 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
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.
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.
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