Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Message passing neural networks (MPNNs) have been shown to suffer from the
phenomenon of over-squashing that causes poor performance for tasks relying on long …

Abstract Graph Convolutional Networks (GCNs) and their variants have experienced
significant attention and have become the de facto methods for learning graph …

Existing popular methods for semi-supervised learning with Graph Neural Networks (such
as the Graph Convolutional Network) provably cannot learn a general class of …

Graph neural networks (GNNs) have emerged as a powerful approach for solving many
network mining tasks. However, learning on large graphs remains a challenge--many …

One of the main challenges in using deep learning-based methods for simulating physical
systems and solving partial differential equations (PDEs) is formulating physics-based data …

Accurate spatial-temporal prediction is a fundamental building block of many real-world
applications such as traffic scheduling and management, environment policy making, and …

As an early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) is able to be
detected by analyzing the brain connectivity networks. For this reason, we devise a new …

Weakly supervised semantic segmentation is receiving great attention due to its low human
annotation cost. In this paper, we aim to tackle bounding box supervised semantic …