Complex Dynamic Event Participant in an Event-Based Social Network: A Three-Dimensional Matching

Abstract

In recent years, event-based social networks (EBSNs), such as Meetup, Eventbrite and Douban, have emerged as a popular new type of social network on which online users can organize and register for offline social events. Existing approaches focus mainly on arrangement strategies that include users and events on an EBSN platform under an offline scenario, where all information is known in advance. However, these strategies ignore the importance of the organizer, and the offline scenarios can be impractical since all elements appear dynamically in reality. Therefore, we consider not only the user and event in the EBSN platform under the online scenario but also the importance of the organizer. In this paper, we study the complex dynamic event participant in an EBSN that considers three elements: users, events and organizers. We first formalize the event arrangement problem in the online scenario through event arrangement through three-dimensional matching (EATDM). Then, we propose a straightforward solution of the EATDM problem, a greedy algorithm with a competitive ratio, and further develop a basis solution, a random algorithm. We also propose a threshold-based algorithm and a weighted-threshold algorithm with a tighter competitive ratio. Finally, we verify the validity and practicability of the proposed algorithms on synthetic and real datasets, and we find that the weighted-threshold algorithm performs better than the random algorithm, the greedy algorithm and the threshold-based algorithm in terms of the total utility value.