Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

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

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

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 …

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

Graph Neural Networks (GNNs) have achieved promising performance in various real-world
applications. However, recent studies have shown that GNNs are vulnerable to adversarial …

Graph neural networks (GNNs) have shown the power in graph representation learning for
numerous tasks. In this work, we discover an interesting phenomenon that although residual …