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Graph Learning with Distributional Edge Layouts
Graph Neural Networks (GNNs) learn from graph-structured data by passing local messages between neighboring nodes along edges on certain topological layouts.
Boosting Graph Pooling with Persistent Homology
Recently, there has been an emerging trend to integrate persistent homology (PH) into graph neural networks (GNNs) to enrich expressive power.
Molecular Conformation Generation via Shifting Scores
Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule.
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