Discovering topically- and temporally-coherent events in interaction networks

TL;DR

This paper introduces a novel method for detecting topically and temporally coherent events in online communication networks by modeling interactions as a meta-graph and solving a prize-collecting Steiner-tree problem with multiple algorithms.

Contribution

It proposes a new interaction meta-graph model and formulates event detection as a prize-collecting Steiner-tree problem, solved with three different algorithms, advancing the analysis of communication data.

Findings

Algorithms effectively detect meaningful events in real datasets.

The methods outperform baseline approaches in synthetic and real data.

Detected events are topically and temporally coherent.

Abstract

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. An important computational challenge is to analyze these data, discover meaningful patterns, and understand what is happening. We consider the problem of mining online communication data and finding top-k temporal events. We define a temporal event to be a coherent topic that is discussed frequently, in a relatively short time span, while the information ow of the event respects the underlying network structure. We construct our model for detecting temporal events in 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 ow. Finding the best temporal event leads to 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. The problem of finding the top- k events among a set of candidate events maps to maximum set-cover problem, and thus, solved by greedy. 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.