Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Node features of graph neural networks (GNNs) tend to become more similar with the
increase of the network depth. This effect is known as over-smoothing, which we …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

Despite the promising representation learning of graph neural networks (GNNs), the
supervised training of GNNs notoriously requires large amounts of labeled data from each …

Large-scale graph training is a notoriously challenging problem for graph neural networks
(GNNs). Due to the nature of evolving graph structures into the training process, vanilla …

Training deep graph neural networks (GNNs) is notoriously hard. Besides the standard
plights in training deep architectures such as vanishing gradients and overfitting, it also …

Dynamical systems minimizing an energy are ubiquitous in geometry and physics. We
propose a gradient flow framework for GNNs where the equations follow the direction of …

The widespread deployment of Graph Neural Networks (GNNs) sparks significant interest in
their explainability, which plays a vital role in model auditing and ensuring trustworthy graph …

Graph Neural Networks (GNNs) have attracted much attention due to their superior learning
capability. Despite the successful applications of GNNs in many areas, their performance …

Deep Graph Networks (DGNs) currently dominate the research landscape of learning from
graphs, due to their efficiency and ability to implement an adaptive message-passing …