Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-
based tasks. However, as mainstream GNNs are designed based on the neural message …

The study of graph neural networks has revealed that they can unleash new applications in
a variety of disciplines using such a basic process that we cannot imagine in the context of …

Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based
learning tasks. Notably, most current GNN architectures operate under the assumption of …

Traditional methods of pre-training, fine-tuning, and ensembling often overlook essential
relational data and task interconnections. To address this gap, our study presents a novel …

Graph neural networks have become the primary graph representation learning paradigm,
in which nodes update their embeddings by aggregating messages from their neighbors …

The imbalanced data classification problem has aroused lots of concerns from both
academia and industry since data imbalance is a widespread phenomenon in many real …

Recently, learning embeddings of nodes in graphs has attracted increasing research
attention. There are two main kinds of graph embedding methods, ie, transductive …

In recent years, graph neural networks (GNNs) have become the most widely used
techniques for irregular data analysis. The core of GNNs lies in feature and/or label …

When it comes to machine learning on graphs, Graph Neural Networks (GNNs) is a potent
tool. By iteratively propagating neural messages along the edges of the input graph, GNNs …

In recent years, Heterogeneous Graph Neural Networks (HGNNs) have been the state-of-the-
art approaches for various tasks on Heterogeneous Graphs (HGs), eg, recommendation and …