70 citations · 94 across the 5 of their papers we have counts for
5 papers
Edge-similarity-aware Graph Neural Networks
Vincent Mallet, Carlos G. Oliver, William L. Hamilton
Graph are a ubiquitous data representation, as they represent a flexible and compact representation. For instance, the 3D structure of RNA can be efficiently represented as $\texti…
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks
Dylan Sandfelder, Priyesh Vijayan, William L. Hamilton
Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-struc…
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
Mikhail Galkin, Etienne Denis, Jiapeng Wu +1
Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector. Such a shallow lookup results in a linear growth of memory c…
Rethinking Graph Transformers with Spectral Attention
Devin Kreuzer, Dominique Beaini, William L. Hamilton +2
In recent years, the Transformer architecture has proven to be very successful in sequence processing, but its application to other data structures, such as graphs, has remained li…
Neural representation and generation for RNA secondary structures
Zichao Yan, William L. Hamilton, Mathieu Blanchette
Our work is concerned with the generation and targeted design of RNA, a type of genetic macromolecule that can adopt complex structures which influence their cellular activities an…