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Event Detection in Activity Networks by Polina Rozenshtein; Aris Anagnostopoulos; Aristides Gionis; Nikolaj Tatti is a scholarly article available to read on EtoBox.

What is Event Detection in Activity Networks about?

With the fast growth of smart devices and social networks, a lot of computing systems collect data that record different types of activities. An important computational challenge is to analyze these data, extract patterns, and understand activity trends. We consider the problem of mining activity networks to identify interesting events, such as a big concert or a demonstration in a city, or a trending keyword in a user community in a social network. We define an event to be a subset of nodes in the network that are close to each other and have high activity levels. We formalize the problem of event detection using two graph-theoretic formulations. The first one captures the compactness of an event using the sum of distances among all pairs of the event nodes. We show that this formulation can be mapped to the MaxCut problem, and thus, it can be solved by applying standard semidefinite programming techniques. The second formulation captures compactness using a minimum-distance tree. This formulation leads to the prize-collecting Steiner-tree problem, which we solve by adapting existing approximation algorithms. For the two problems we introduce, we also propose efficient and effecti

Author

Polina Rozenshtein; Aris Anagnostopoulos; Aristides Gionis; Nikolaj Tatti

Publisher

ACM

Published

2014

Language

EN