Discovering topically- and temporally-coherent events in interaction networks.

With the increasing use of online communication platforms, such as email, Twitter, and messaging applications, we are faced with a growing amount of data that combine content (what is said), time (when), and user (by whom) information. Discovering meaningful patterns and understand what is happening in this data is an important challenge. We consider the problem of mining online communication data and finding top-(k ) temporal events. A temporal event is a coherent topic that is discussed frequently in a relatively short time span, while its information flow respects the underlying network.