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 …

Computational approaches have revolutionized the field of drug discovery, collectively
known as Computer-Assisted Drug Design (CADD). Advancements in computing power …

The bioactivity of compounds plays an important role in drug development and discovery.
Existing machine learning approaches have poor generalizability in bioactivity prediction …

Graph clustering, which learns the node representations for effective cluster assignments, is
a fundamental yet challenging task in data analysis and has received considerable attention …

Accurate prediction of molecular properties is an important topic in drug discovery. Recent
works have developed various representation schemes for molecular structures to capture …

Graph classification is a crucial task in many real-world multimedia applications, where
graphs can represent various multimedia data types such as images, videos, and social …

Self-supervised graph representation learning has recently shown considerable promise in
a range of fields, including bioinformatics and social networks. A large number of graph …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …

Next Point-of-Interest (POI) recommendation is a critical task in location-based services that
aim to provide personalized suggestions for the user's next destination. Previous works on …

This paper studies zero-shot node classification, which aims to predict new classes (ie,
unseen classes) of nodes in a graph. This problem is challenging yet promising in a variety …