Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

The goal of graph clustering is to partition vertices in a large graph into different clusters
based on various criteria such as vertex connectivity or neighborhood similarity. Graph …

Social network analysis has gained significant attention in recent years, largely due to the
success of online social networking and media-sharing sites, and the consequent …

In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern
that describe the evolution of large networks over time, at a local level. Given a sequence of …

Social networks, sensor networks, biological networks, and many other information networks
can be modeled as a large graph. Graph vertices represent entities, and graph edges …

A number of sensor applications in recent years collect data which can be directly
associated with human interactions. Some examples of such applications include GPS …

Given a large network, changing over time, how can we find patterns and anomalies? We
propose Com2, a novel and fast, incremental tensor analysis approach, which can discover …

Social media anomaly detection is of critical importance to prevent malicious activities such
as bullying, terrorist attack planning, and fraud information dissemination. With the recent …

Many types of human mobility data, such as flows of taxicabs, card swiping data of subways,
bike trip data and Call Details Records (CDR), can be modeled by a Spatio-Temporal Graph …

Представлен обзор работ, посвященных проблеме анализа компьютерных
социальных сетей. Существует четыре основных направления исследований в данной …