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

Unlike previous studies on the Metaverse based on Second Life, the current Metaverse is
based on the social value of Generation Z that online and offline selves are not different …

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 …

Travel-time prediction constitutes a task of high importance in transportation networks, with
web mapping services like Google Maps regularly serving vast quantities of travel time …

Abstract Graph Neural Networks (GNNs) have been shown to be effective models for
different predictive tasks on graph-structured data. Recent work on their expressive power …

Even though neural networks enjoy widespread use, they still struggle to learn the basic
laws of physics. How might we endow them with better inductive biases? In this paper, we …

Particle physics is a branch of science aiming at discovering the fundamental laws of matter
and forces. Graph neural networks are trainable functions which operate on graphs—sets of …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

Graph neural networks have-enabled the application of deep learning to problems that can
be described by graphs, which are found throughout the different fields of sci-ence, from …

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks,
especially in a way that generalises out of distribution. While recent years have seen a surge …