Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Lots of learning tasks require dealing with graph data which contains rich relation
information among elements. Modeling physics systems, learning molecular fingerprints …

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 …

In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …

Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …

We show that standard Transformers without graph-specific modifications can lead to
promising results in graph learning both in theory and practice. Given a graph, we simply …

Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …

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 …

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 …

The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …