3.2k citations · 6.9k across the 41 of their papers we have counts for
5 papers · 1 filter
Transformers are efficient hierarchical chemical graph learners
Zihan Pengmei, Zimu Li, Chih-chan Tien +2
Transformers, adapted from natural language processing, are emerging as a leading approach for graph representation learning. Contemporary graph transformers often treat nodes or e…
P-Tensors: a General Formalism for Constructing Higher Order Message Passing Networks
Andrew Hands, Tianyi Sun, Risi Kondor
Several recent papers have proposed increasing the expressive power of graph neural networks by exploiting subgraphs or other topological structures. In parallel, researchers have…
Modeling Polypharmacy and Predicting Drug-Drug Interactions using Deep Generative Models on Multimodal Graphs
Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Latent representations of drugs and their targets produced by contemporary graph autoencoder models have proved useful in predicting many types of node-pair interactions on large n…
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures
Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to det…
Fast Temporal Wavelet Graph Neural Networks
Duc Thien Nguyen, Manh Duc Tuan Nguyen, Truong Son Hy +1
Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to the highly intricate…