Though the sheer volume of data that is collected is immense, it is the rare categories that
are often the most important in many high-impact domains, ranging from financial fraud …

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 …

Anomaly subgraph detection is a crucial task in various real-world applications, including
identifying high-risk areas, detecting river pollution, and monitoring disease outbreaks. Early …

Anomaly subgraph detection has been widely used in various applications, ranging from
cyber attack in computer networks to malicious activities in social networks. Despite an …

Anomaly subgraph detection is an important problem that has been well researched in
various applications, ranging from cyberattacks in computer networks to malicious activities …

Anomaly subgraph detection is a vital task in various real applications. However, with the
advancement of AI technology, it faces new challenges: 1) Anomaly features are often …

Anomaly detection using a network-based approach is one of the most efficient ways to
identify abnormal events such as fraud, security breaches, and system faults in a variety of …

The detection of anomaly subgraphs naturally appears in various real-life tasks, yet label
noise seriously interferes with the result. As a motivation for our work, we focus on …

Anomaly subgraph detection has been widely used in various scenarios and fields (eg,
congestion related to passenger cars). Most existing methods for discovering anomalies in …

In the age of big data, multiple networks naturally appear in a variety of domains, such as
social network analysis, bioinformatics, finance, infrastructure and so on. Network alignment …