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Found 4 papers, 4 papers with code
On the Stability of Expressive Positional Encodings for Graphs
Despite many attempts to address non-uniqueness, most methods overlook stability, leading to poor generalization on unseen graph structures.
Molecular Property Prediction
Out-of-Distribution Generalization
+1
Is Distance Matrix Enough for Geometric Deep Learning?
In this work, we first construct families of novel and symmetric geometric graphs that Vanilla DisGNN cannot distinguish even when considering all-pair distances, which greatly expands the existing counterexample families.
Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs
To the best of our knowledge, it is the first linear-time GNN model that can count 6-cycles with theoretical guarantees.
3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design
The main computational challenges include: 1) the generation of linkers is conditional on the two given molecules, in contrast to generating full molecules from scratch in previous works; 2) linkers heavily depend on the anchor atoms of the two molecules to be connected, which are not known beforehand; 3) 3D structures and orientations of the molecules need to be considered to avoid atom clashes, for which equivariance to E(3) group are necessary.
