Routine clinical visits of a patient produce not only image data, but also non-image data
containing clinical information regarding the patient, ie, medical data is multi-modal in …

Graph neural networks (GNNs) have demonstrated remarkable success for semisupervised
node classification. However, these GNNs are still limited to the conventionally …

Abstract Graph Neural Networks (GNNs) have exhibited significant success in various
applications, but they face challenges when labeled nodes are limited. A novel self …

Question Answering (QA) effectively evaluates language models' reasoning and knowledge
depth. While QA datasets are plentiful in areas like general domain and biomedicine …

Inspired by the recent success of self-supervised methods applied on images, self-
supervised learning on graph structured data has seen rapid growth especially centered on …

Graph convolution is a fundamental building block for many deep neural networks on graph-
structured data. In this paper, we introduce a simple, yet very effective graph convolutional …

Graph Neural Networks (GNNs) are powerful tools for addressing learning problems on
graph structures, with a wide range of applications in molecular biology and social networks …

Existing graph neural network methods usually depend on a large amount of labeled data,
but labeled data is often scarce in the real world. In the case of less labeled data, utilizing …

Graph-based semi-supervised node classification has been shown to become a state-of-the-
art approach in many applications with high research value and significance. Most existing …

With the gradual development of deep learning-related technologies, Graph Neural
Networks (GNNs) have achieved great success in graph representation learning, promoted …