In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Machine learning on graphs has been extensively studied in both academic and industry.
However, as the literature on graph learning booms with a vast number of emerging …

Graph classification is an important problem with applications across many domains, like
chemistry and bioinformatics, for which graph neural networks (GNNs) have been state-of …

Graph classification is an important problem with applications across many domains, for
which graph neural networks (GNNs) have been state-of-the-art (SOTA) methods. In the …

Recently, graph neural architecture search (GNAS) frameworks have been successfully
used to automatically design the optimal neural architectures for many problems such as …

Drug–drug interaction (DDI) has attracted widespread attention because when incompatible
drugs are taken together, DDI will lead to adverse effects on the body, such as drug …

In recent years, graph neural networks (GNNs) based on neighborhood aggregation
schemes have become a promising method in various graph-based applications. To solve …

Graph-structured physical mechanisms are ubiquitous in real-world scenarios, thus
revealing underneath formulas is of great importance for scientific discovery. However …

Graph machine learning has been extensively studied in both academic and industry.
However, as the literature on graph learning booms with a vast number of emerging …