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 …

Recent technological advances allow for the collection of massive data in the study of
complex phenomena over time and/or space in various fields. Many of these data involve …

Graph analysis performs many random reads and writes, thus, these workloads are typically
performed in memory. Traditionally, analyzing large graphs requires a cluster of machines …

How much has a network changed since yesterday? How different is the wiring of Bob's
brain (a left-handed male) and Alice's brain (a right-handed female), and how is it different …

We consider a general formulation of the multiple change-point problem, in which the data is
assumed to belong to a set equipped with a positive semidefinite kernel. We propose a …

We study the problem of change point localization in dynamic networks models. We assume
that we observe a sequence of independent adjacency matrices of the same size, each …

We propose a new framework for the detection of change-points in online, sequential data
analysis. The approach utilizes nearest neighbor information and can be applied to …

Edge streams are commonly used to capture interactions in dynamic networks, such as
email, social, or computer networks. The problem of detecting anomalies or rare events in …

Anomaly detection in sequence data is becoming more and more important in a wide variety
of application domains such as credit card fraud detection, health care in medical field, and …

The problem of identifying anomalies in dynamic networks is a fundamental task with a wide
range of applications. However, it raises critical challenges due to the complex nature of …