Unsupervised graph anomaly detection has been widely used in real-world applications.
Existing methods primarily focus on local inconsistency mining (LIM), based on the intuition …

Many anomaly detection applications naturally pro-duce datasets that can be represented
as bipartite graphs (user-interaction-item graphs). These graph datasets are usually sup …

Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary
objective of identifying anomalous nodes that deviate significantly from the majority. This …

In complex systems like social media and financial transactions, diverse entities (users,
groups, products) interact through a multitude of relationships (friendships, comments …

**年来, 图嵌入学**已成为信息网络分析领域最常用的技术之一, 其将网络对象嵌入到低维稠密
向量空间的同时保留网络结构和内容特征并应用于下游分析任务. 然而大多数现实网络是由多种 …

With the frequent occurrence of network security incidents in recent years, it has become
very important to detect anomalous behaviour in networks as early and accurately as …