EDMS: A system for efficient processing distance-aware influence maximization

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9877 LNCS pp. 462 - 466
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© Springer International Publishing AG 2016. As a key problem in viral marketing, influence maximization has been widely studied in the literature. It aims to find a set of k users in a social network, which can maximize the influence spread under a certain propagation model. With the proliferation of geo-social networks, location-aware promotion is becoming more and more necessary in real applications. However, the importance of the distance between users and the promoted locations is underestimated in the existing work. For example, when promoting a local store, the owner may prefer to influence more people that are close to the store instead of people that are far away. In this demonstration, we propose EDMS, a centralized system that efficiently processes the distance-aware influence maximization problem. To meet the online requirements, we combine different pruning strategies and the best first search algorithm to significantly reduce the search space. We present a prototype, which provides users with a web interface to issue queries and visualize the search results in real time.
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