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 …

Graph networks can model data observed across different levels of biological systems that
span from population graphs (with patients as network nodes) to molecular graphs that …

Abstract Protein-Protein Interactions (PPIs) are fundamental means of functions and
signalings in biological systems. The massive growth in demand and cost associated with …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

Graph neural networks have been widely used on modeling graph data, achieving
impressive results on node classification and link prediction tasks. Yet, obtaining an …

Graph representation learning has attracted increasing research attention. However, most
existing studies fuse all structural features and node attributes to provide an overarching …

Given the input graph and its label/property, several key problems of graph learning, such as
finding interpretable subgraphs, graph denoising and graph compression, can be attributed …

Potential Drug-Drug Interactions (DDI) occur while treating complex or co-existing diseases
with drug combinations, which may cause changes in drugs' pharmacological activity …

With the development of foundation models such as large language models, zero-shot
transfer learning has become increasingly significant. This is highlighted by the generative …

Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs
is one of the most commonly used approaches for learning the representation of users and …