Graph neural networks have pushed state-of-the-arts in graph classifications recently.
Typically, these methods are studied within the context of supervised end-to-end training …

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 a specific case of graph transfer learning, unsupervised domain adaptation on graphs
aims for knowledge transfer from label-rich source graphs to unlabeled target graphs …

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

In recent years, histopathological whole slide image (WSI)-based survival analysis has
attracted much attention in medical image analysis. In practice, WSIs usually come from …

Graph classification has gained growing attention in the graph machine learning community
and a variety of semi-supervised methods have been developed to reduce the high cost of …

Graph domain adaptation models have become instrumental in addressing cross-network
learning problems due to their ability to transfer abundant label and structural knowledge …

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 …

Question answering over knowledge base (KBQA) enables users to query over the
knowledge base without the need to know the details. A range of existing KBQA approaches …

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