Unsupervised graph representation learning (UGRL) has drawn increasing research
attention and achieved promising results in several graph analytic tasks. Relying on the …

Given the prevalence of large-scale graphs in real-world applications, the storage and time
for training neural models have raised increasing concerns. To alleviate the concerns, we …

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 …

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

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

Most existing deep learning models are trained based on the closed-world assumption,
where the test data is assumed to be drawn iid from the same distribution as the training …

The huge volume of emerging graph datasets has become a double-bladed sword for graph
machine learning. On the one hand, it empowers the success of a myriad of graph neural …

Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …

We study graph data augmentation by mixup, which has been used successfully on images.
A key operation of mixup is to compute a convex combination of a pair of inputs. This …

Recent years, graph contrastive learning (GCL), which aims to learn representations from
unlabeled graphs, has made great progress. However, the existing GCL methods mostly …