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 …

The role of data in building AI systems has recently been significantly magnified by the
emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model …

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 …

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

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

Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained
increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian …

Recently, many carefully designed graph representation learning methods have achieved
impressive performance on either strong heterophilic or homophilic graphs, but not both …

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 …

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 …

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 …