Self-supervised learning on graph-structured data has drawn recent interest for learning
generalizable, transferable and robust representations from unlabeled graphs. Among …

Deep learning on graphs has attracted significant interests recently. However, most of the
works have focused on (semi-) supervised learning, resulting in shortcomings including …

Deep learning on graphs has recently achieved remarkable success on a variety of tasks,
while such success relies heavily on the massive and carefully labeled data. However …

Background Drug–target affinity (DTA) prediction is a critical step in the field of drug
discovery. In recent years, deep learning-based methods have emerged for DTA prediction …

Review-based recommender systems explore semantic aspects of users' preferences by
incorporating user-generated reviews into rating-based models. Recent works have …

Recent contrastive based unsupervised object recognition methods leverage a Siamese
architecture, which has two branches composed of a backbone, a projector layer, and an …

Graph pooling plays a vital role in learning graph embeddings. Due to the lack of label
information, unsupervised graph pooling has received much attention, primarily via mutual …

In recent years, graph representation learning has achieved remarkable success while
suffering from low-quality data problems. As a mature technology to improve data quality in …

Medical image datasets are often imbalanced due to biases in data collection and limitations
in acquiring data for rare conditions. Addressing class imbalance is crucial for developing …

Studies on graph contrastive learning, which is an effective way of self-supervision, have
achieved excellent experimental performance. Most existing methods generate two …