The power of any kind of network approach lies in the ability to simplify a complex system so
that one can better understand its function as a whole. Sometimes it is beneficial, however …

Large volumes of event data are becoming increasingly available in a wide variety of
applications, such as healthcare analytics, smart cities and social network analysis. The …

We develop a warped correlation finder to identify correlated user accounts in social media
websites such as Twitter. The key observation is that humans cannot be highly synchronous …

Despite rapid development, current bot detection models still face challenges in dealing with
incomplete data and cross-platform applications. In this paper, we propose BotBuster, a …

Many news outlets allow users to contribute comments on topics about daily world events.
News articles are the seeds that spring users' interest to contribute content, that is …

This study explains how bots interact with human users and influence conversational
networks on Twitter. We analyze a high-stakes political environment, the UK general …

Review fraud is a pervasive problem in online commerce, in which fraudulent sellers write or
purchase fake reviews to manipulate perception of their products and services. Fake reviews …

Cascades are an accepted model to capturing how information diffuses across social
network platforms. A large body of research has been focused on dissecting the anatomy of …

Event sequence prediction has wide applications on economics, electronic health and social
media monitoring. Accurate prediction of event sequences can help provide better service to …

We introduce a framework for the modeling of sequential data capturing pathways of varying
lengths observed in a network. Such data are important, eg, when studying click streams in …