Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

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 learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …

Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have
facilitated various applications from drug discovery to recommender systems. Nevertheless …

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 …

The recent methods for cross-network node classification mainly exploit graph neural
networks (GNNs) as feature extractor to learn expressive graph representations across the …

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 …