Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

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 …

Linked or networked data are ubiquitous in many applications. Examples include web data
or hypertext documents connected via hyperlinks, social networks or user profiles connected …

The effectiveness of knowledge transfer using classification algorithms depends on the
difference between the distribution that generates the training examples and the one from …

Anomaly detection is a crucial aspect for both safety and efficiency of modern process
industries. This paper proposes a two-steps methodology for anomaly detection in industrial …

Outlier detection can usually be considered as a pre-processing step for locating, in a data
set, those objects that do not conform to well-defined notions of expected behavior. It is very …

Outlier detection is of great importance in industry as unexpected errors or faults, abnormal
behaviours or phenomena, etc. can occur due to a variety of human, system, and …

Outlier detection has been well studied due to its wide applications in both academia and
industry, among which graph-based methods have drawn extensive attention in recent years …

Grid-based approaches render an efficient framework for data clustering in the presence of
incomplete, inexplicit, and uncertain data. This paper proposes an entropy-based grid …

The technique of spectral clustering is widely used to segment a range of data from graphs
to images. Our work marks a natural progression of spectral clustering from the original …