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 …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Graph (structure) augmentation aims to perturb the graph structure through heuristic or
probabilistic rules, enabling the nodes to capture richer contextual information and thus …

Graph machine learning has been extensively studied in both academia and industry.
Although booming with a vast number of emerging methods and techniques, most of the …

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

Graph contrastive learning (GCL), as an emerging self-supervised learning technique on
graphs, aims to learn representations via instance discrimination. Its performance heavily …

Graph Neural Networks (GNNs) have achieved unprecedented success in identifying
categorical labels of graphs. However, most existing graph classification problems with …

Recent years have witnessed remarkable success achieved by graph neural networks
(GNNs) in many real-world applications such as recommendation and drug discovery …

Recently, contrastiveness-based augmentation surges a new climax in the computer vision
domain, where some operations, including rotation, crop, and flip, combined with dedicated …