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

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

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

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 …

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 …

The objective of topic inference in research proposals aims to obtain the most suitable
disciplinary division from the discipline system defined by a funding agency. The agency will …

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 …

Distribution shifts on graphs--the discrepancies in data distribution between training and
employing a graph machine learning model--are ubiquitous and often unavoidable in real …

Distribution shifts on graphs--the data distribution discrepancies between training and
testing a graph machine learning model, are often ubiquitous and unavoidable in real-world …