Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Anomaly detection is an important problem with multiple applications, and thus has been
studied for decades in various research domains. In the past decade there has been a …

Massive and dynamic networks arise in many practical applications such as social media,
security and public health. Given an evolutionary network, it is crucial to detect structural …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Evolutionary network analysis has found an increasing interest in the literature because of
the importance of different kinds of dynamic social networks, email networks, biological …

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

With the development of hardware technology, we can collect increasingly reliable time
series data, in which time series anomaly detection is an important task to find problems in …

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 …

In the last decade, outlier detection for temporal data has received much attention from data
mining and machine learning communities. While other works have addressed this problem …

Accurate detection of anomalies in multivariate time series data has attracted much attention
due to its importance in a wide range of applications. Since it is difficult to obtain accurately …