Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …

Generalizable, transferrable, and robust representation learning on graph-structured data
remains a challenge for current graph neural networks (GNNs). Unlike what has been …

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which
model graphs as collections of subgraphs. So far, the design space of possible Subgraph …

Enabling effective and efficient machine learning (ML) over large-scale graph data (eg,
graphs with billions of edges) can have a great impact on both industrial and scientific …

Leading graph contrastive learning (GCL) methods perform graph augmentations in two
fashions:(1) randomly corrupting the anchor graph, which could cause the loss of semantic …

Abstract Graph Neural Networks (GNNs) are limited in their expressive power, struggle with
long-range interactions and lack a principled way to model higher-order structures. These …