In recent years, there have been remarkable advancements in node classification achieved
by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels …

Abstract Graph Neural Networks (GNNs) are crucial for machine learning applications with
graph-structured data, but their success depends on sufficient labeled data. We present a …

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 …

Graph neural networks (GNNs) have become a popular approach for semi-supervised graph
representation learning. GNNs research has generally focused on improving …

Abstract Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-
supervised node classification tasks. However, recent studies have revealed various biases …

Graph Neural Networks have demonstrated great success in various fields of multimedia.
However, the distribution shift between the training and test data challenges the …

Graph Neural Networks (GNNs) have demonstrated outstanding results in many graph-
based deep-learning tasks. However, training GNNs on a large graph can be difficult due to …

Abstract Graph Neural Networks (GNNs) are deep learning models specifically designed for
analyzing graph-structured data, capturing complex relationships and structures to improve …

In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown
effectiveness. However, a common challenge in these applications is the underutilization of …

Many computer vision and machine learning problems are modelled as learning tasks on
graphs, where graph neural networks (GNNs) have emerged as a dominant tool for learning …