Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on
heterogeneous graphs. Typical HGNNs require repetitive message passing during training …

Graph, such as citation networks, social networks, and transportation networks, are
prevalent in the real world. Graph Neural Networks (GNNs) have gained widespread …

Graph pre-training has been concentrated on graph-level on small graphs (eg, molecular
graphs) or learning node representations on a fixed graph. Extending graph pre-trained …

Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence,
exhibiting remarkable abilities to generalize across a wide array of tasks and applications …

Semi-supervised learning has gained significant attention in the field of remote sensing due
to its ability to effectively leverage both a limited number of labeled samples and a large …

As large language models (LLMs) continue to evolve, ensuring their alignment with human
goals and values remains a pressing challenge. A key concern is\textit {instrumental …

In recent years, Graph Neural Networks (GNNs) have achieved significant success in graph-
based tasks. However, they still face challenges in complex scenarios, particularly in …

Graph representation learning is a fundamental research issue in various domains of
applications, of which the inductive learning problem is particularly challenging as it requires …