Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and
natural language processing (NLP). One of the most notable advancements of LLMs is that a …

Semi-supervised graph domain adaptation, as a subfield of graph transfer learning, seeks to
precisely annotate unlabeled target graph nodes by leveraging transferable features …

As graph representation learning often suffers from label scarcity problems in real-world
applications, researchers have proposed graph domain adaptation (GDA) as an effective …

Affective brain-computer interface is an important part of realizing emotional human–
computer interaction. However, existing objective individual differences among subjects …

Spiking Graph Networks (SGNs) have garnered significant attraction from both researchers
and industry due to their ability to address energy consumption challenges in graph …

Graph domain adaptation, which falls under the umbrella of graph transfer learning, involves
transferring knowledge from a labeled source graph to improve prediction accuracy on an …

Graph neural networks (GNNs) have demonstrated remarkable success in numerous graph
analytical tasks. Yet, their effectiveness is often compromised in real-world scenarios due to …

Graph Machine Learning (Graph ML) has witnessed substantial advancements in recent
years. With their remarkable ability to process graph-structured data, Graph ML techniques …

Domain adaptation has demonstrated success in classification of multi-center autism
spectrum disorder (ASD). However, current domain adaptation methods primarily focus on …

Semi-supervised graph domain adaptation, as a branch of graph transfer learning, aims to
annotate unlabeled target graph nodes by utilizing transferable knowledge learned from a …