54 citations · 172 across the 15 of their papers we have counts for
6 papers · 1 filter
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Minghao Guo, Veronika Thost, Samuel W Song +4
The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the…
Transformers over Directed Acyclic Graphs
Yuankai Luo, Veronika Thost, Lei Shi
Transformer models have recently gained popularity in graph representation learning as they have the potential to learn complex relationships beyond the ones captured by regular gr…
Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li +3
The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets…
Improving Inductive Link Prediction Using Hyper-Relational Facts
Mehdi Ali, Max Berrendorf, Mikhail Galkin +4
For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put i…
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
Arthur Feeney, Rishabh Gupta, Veronika Thost +4
Sampling is an established technique to scale graph neural networks to large graphs. Current approaches however assume the graphs to be homogeneous in terms of relations and ignore…
Directed Acyclic Graph Neural Networks
Veronika Thost, Jie Chen
Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they…