Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

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 …

Attributed networks are ubiquitous and form a critical component of modern information
infrastructure, where additional node attributes complement the raw network structure in …

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Online social networks have received a dramatic increase of interest in the last decade due
to the growth of Internet and Web 2.0. They are among the most popular sites on the Internet …

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …

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 …

Graph anomaly detection, here, aims to find rare patterns that are significantly different from
other nodes. Attributed graphs containing complex structure and attribute information are …

Anomalies in online social networks can signify irregular, and often illegal behaviour.
Detection of such anomalies has been used to identify malicious individuals, including …

Attributed networks are pervasive in different domains, ranging from social networks, gene
regulatory networks to financial transaction networks. This kind of rich network …