Machine Learning and Deep Learning methods are widely adopted across financial
domains to support trading activities, mobile banking, payments, and making customer credit …

With multiple components and relations, financial data are often presented as graph data,
since it could represent both the individual features and the complicated relations. Due to …

Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …

Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in
recent years, revealing the suspiciousness of nodes by aggregating their neighborhood …

With a long history of traditional Graph Anomaly Detection (GAD) algorithms and recently
popular Graph Neural Networks (GNNs), it is still not clear (1) how they perform under a …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

Since many real world networks are evolving over time, such as social networks and user-
item networks, there are increasing research efforts on dynamic network embedding in …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

With the widespread digitization of finance and the increasing popularity of cryptocurrencies,
the sophistication of fraud schemes devised by cybercriminals is growing. Money laundering …

Abstract Graph Anomaly Detection (GAD) has recently become a hot research spot due to its
practicability and theoretical value. Since GAD emphasizes the application and the rarity of …