Since the onset of the COVID-19 pandemic, there has been a growing interest in studying
epidemiological models. Traditional mechanistic models mathematically describe the …

Following a fast initial breakthrough in graph-based learning, Graph Neural Networks
(GNNs) have reached a widespread application in many science and engineering fields …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Large Language Models (LLMs) have demonstrated remarkable success across various
domains. However, despite their promising performance in numerous real-world …

Diffusion models, as a novel generative paradigm, have achieved remarkable success in
various image generation tasks such as image inpainting, image-to-text translation, and …

Graph neural networks (GNNs) are powerful graph-based deep-learning models that have
gained significant attention and demonstrated remarkable performance in various domains …

The widespread deployment of Graph Neural Networks (GNNs) sparks significant interest in
their explainability, which plays a vital role in model auditing and ensuring trustworthy graph …

Graph Neural Networks (GNNs) have achieved promising results in various tasks such as
node classification and graph classification. Recent studies find that GNNs are vulnerable to …

This paper is the first to use contrastive learning to improve the robustness of graph
representation learning for signed bipartite graphs, which are commonly found in social …

Graph neural networks (GNNs) have become pivotal in various critical decision-making
scenarios due to their exceptional performance. However, concerns have been raised that …