Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

As industries become automated and connectivity technologies advance, a wide range of
systems continues to generate massive amounts of data. Many approaches have been …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

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 …

Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …

Anomalies represent deviations from the intended system operation and can lead to
decreased efficiency as well as partial or complete system failure. As the causes of …

The Industrial Internet of Things (IIoT) plays an important role in digital transformation of
traditional industries toward Industry 4.0. By connecting sensors, instruments, and other …

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 …