Retrieving, analyzing, and processing large data can be challenging. An effective and
efficient mechanism for overcoming these challenges is to cluster the data into a compact …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

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

Recent years, graph contrastive learning (GCL), which aims to learn representations from
unlabeled graphs, has made great progress. However, the existing GCL methods mostly …

Graph convolutional networks (GCNs) have achieved impressive success in many graph
related analytics tasks. However, most GCNs only work in a single domain (graph) …

Deep learning (DL) has brought many new trends for hyperspectral image classification
(HIC). Graph neural networks (GNNs) are models that fuse DL and structured data. Although …

Transfer learning on graphs drawn from varied distributions (domains) is in great demand
across many applications. Emerging methods attempt to learn domain-invariant …

Transfer learning refers to the transfer of knowledge or information from a relevant source
domain to a target domain. However, most existing transfer learning theories and algorithms …

Graph neural networks (GNNs) have achieved impressive impressions for graph-related
tasks. However, most GNNs are primarily studied under the cases of signal domain with …

Unsupervised Graph Domain Adaptation (UGDA) aims to transfer knowledge from a labelled
source graph to an unlabelled target graph in order to address the distribution shifts …