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Found 3 papers, 1 papers with code
On Structural Expressive Power of Graph Transformers
Graph Transformer has recently received wide attention in the research community with its outstanding performance, yet its structural expressive power has not been well analyzed.
Hierarchical Transformer for Scalable Graph Learning
Graph Transformer is gaining increasing attention in the field of machine learning and has demonstrated state-of-the-art performance on benchmarks for graph representation learning.
Equivalent Distance Geometry Error for Molecular Conformation Comparison
Straight-forward conformation generation models, which generate 3-D structures directly from input molecular graphs, play an important role in various molecular tasks with machine learning, such as 3D-QSAR and virtual screening in drug design.
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