Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

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 …

The ability to incrementally learn new classes is vital to all real-world artificial intelligence
systems. A large portion of high-impact applications like social media, recommendation …

Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are
prominent techniques for homogeneous and heterogeneous graph representation learning …

Text classification is a fundamental problem in information retrieval with many real-world
applications, such as predicting the topics of online articles and the categories of e …

Graphs can model complex relationships between objects, enabling a myriad of Web
applications such as online page/article classification and social recommendation. While …

Node classification is of great importance among various graph mining tasks. In practice,
real-world graphs generally follow the long-tail distribution, where a large number of classes …

Graph representation learning has attracted tremendous attention due to its remarkable
performance in many real-world applications. However, prevailing supervised graph …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …