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 …

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 …

Dense subgraph discovery has proven useful in various applications of temporal networks.
We focus on a special class of temporal networks whose nodes and edges are kept fixed …

Semantic Web technologies and in particular Linked Open Data provide a means for sharing
knowledge about cities as physical, social, and technical systems, so enabling the …

Given a time-evolving network, how can we detect communities over periods of high internal
and low external interactions? To address this question we generalize traditional local …

In this work, we propose a new, fast and scalable method for anomaly detection in large time-
evolving graphs. It may be a static graph with dynamic node attributes (eg time-series), or a …

Communities are essential building blocks of complex networks enjoying significant
research attention in terms of modeling and detection algorithms. Common across models is …

Recent advances in data-acquisition technologies have equipped team coaches and sports
analysts with the capability of collecting and analyzing detailed data of team activity in the …

Anomaly detection in dynamic networks aims to find network elements (eg, nodes, edges,
subgraphs, change points) with significantly different behaviors from the vast majority, it can …

Many complex networked systems, both natural and human-made, exhibit periodic behavior
driven by underlying seasonal processes: election cycles and regular sporting events in …