Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

As graph neural networks are becoming more and more powerful and useful in the field of
drug discovery, many pharmaceutical companies are getting interested in utilizing these …

Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …

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 …

This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

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 …

We consider the problem of explaining the predictions of graph neural networks (GNNs),
which otherwise are considered as black boxes. Existing methods invariably focus on …