Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

The rapid growth of graph data poses significant challenges in storage, transmission, and
particularly the training of graph neural networks (GNNs). To address these challenges …

A Gentle Introduction to Graph Neural Networks Distill About Prize Submit A Gentle Introduction
to Graph Neural Networks Neural networks have been adapted to leverage the structure and …

We study how neural networks trained by gradient descent extrapolate, ie, what they learn
outside the support of the training distribution. Previous works report mixed empirical results …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …

We address two fundamental questions about graph neural networks (GNNs). First, we
prove that several important graph properties, eg, shortest/longest cycle, diameter, or certain …

We perform a careful, thorough, and large scale empirical study of the correspondence
between wide neural networks and kernel methods. By doing so, we resolve a variety of …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …