Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Abstract Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As
one of the key components for GNN design is to select a tailored spectral filter, we take the …

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 …

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 …

Attributed network embedding is the task to learn a lower dimensional vector representation
of the nodes of an attributed network, which can be used further for downstream network …

Effectively detecting anomalous nodes in attributed networks is crucial for the success of
many real-world applications such as fraud and intrusion detection. Existing approaches …

Graph neural networks are essential in mining complex relationships in graphs. However,
most methods ignore the global location information of nodes and the discrepancy between …

Abstract Graph Neural Networks (GNNs) are powerful tools for graph representation
learning. Despite their rapid development, GNNs also face some challenges, such as over …

Anomaly detection (such as telecom fraud detection and medical image detection) has
attracted the increasing attention of people. The complex interaction between multiple …

Microservices are becoming the defacto design choice for software architecture. It involves
partitioning the software components into finer modules such that the development can …