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 …

Many real‐world networks are intimately organized according to a community structure.
Much research effort has been devoted to develop methods and algorithms that can …

This survey provides an overview of higher-order tensor decompositions, their applications,
and available software. A tensor is a multidimensional or-way array. Decompositions of …

Many data are modeled as tensors, or multi dimensional arrays. Examples include the
predicates (subject, verb, object) in knowledge bases, hyperlinks and anchor texts in the …

For the low-rank approximation of time-dependent data matrices and of solutions to matrix
differential equations, an increment-based computational approach is proposed and …

Modern applications such as Internet traffic, telecommunication records, and large-scale
social networks generate massive amounts of data with multiple aspects and high …

Traditional spectral-based methods such as PCA are popular for anomaly detection in a
variety of problems and domains. However, if data includes tensor (multiway) structure (eg …

How do we find patterns in author-keyword associations, evolving over time? Or in data
cubes (tensors), with product-branchcustomer sales information? And more generally, how …

How can we find useful patterns and anomalies in large scale real-world data with multiple
attributes? For example, network intrusion logs, with (source-ip, target-ip, port-number …

In recent years, measurement or collection of heterogeneous sets of data such as those
containing scalars, waveform signals, images, and even structured point clouds, has …