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. Our method consists of two steps. We first introduce the notion of interaction meta-graph, which connects associated interactions. Using this notion, we define a temporal event to be a subset of interactions that (i) are topically and temporally close and (ii) correspond to a tree that captures the information flow. Finding the best temporal event leads to a budget version of the prize-collecting Steiner-tree (PCST) problem, which we solve using three different methods: a greedy approach, a dynamic-programming algorithm, and an adaptation to an existing approximation algorithm. Finding the top-k events maps to a maximum set-cover problem, and thus, solved by greedy algorithm. We compare and analyze our algorithms in both synthetic and real datasets, such as Twitter and email communication. The results show that our methods are able to detect meaningful temporal events.