Abstract Graph Neural Networks (GNNs) have shown exceptional performance in the task of
link prediction. Despite their effectiveness, the high latency brought by non-trivial …

Explaining machine learning models is an important and increasingly popular area of
research interest. The Shapley value from game theory has been proposed as a prime …

A recent focal area in the space of graph neural networks (GNNs) is graph self-supervised
learning (SSL), which aims to derive useful node representations without labeled data …

Review is an essential piece of information that influences users' decisions, but excessively
long reviews not only degrade the user experience but also affect the accuracy of the …

Transformers have recently emerged as powerful neural networks for graph learning,
showcasing state-of-the-art performance on several graph property prediction tasks …

Self-supervised learning on graphs has made large strides in achieving great performance
in various downstream tasks. However, many state-of-the-art methods suffer from a number …

Collaborative filtering (CF) has exhibited prominent results for recommender systems and
been broadly utilized for real-world applications. A branch of research enhances CF …

Friend recommendation systems in online social and professional networks such as
Snapchat helps users find friends and build connections, leading to better user engagement …

Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown
state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show …

While revolutionizing social networks, recommendation systems, and online web services,
graph neural networks are vulnerable to adversarial attacks. Recent state-of-the-art …