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Sample location selection for efficient distance-aware influence maximization in geo-social networks
conference contribution
posted on 2018-01-01, 00:00 authored by Ming Zhong, Qian Zeng, Yuanyuan Zhu, Jianxin LiJianxin Li, Tieyun QianIn geo-social networks, the distances of users to a location play an important role in populating the business or campaign at the location. Thereby, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM computation heavily relies on the sample location selection, because the online seeding performance is sensitive to the distance between sample location and promoted location, and the offline precomputation performance is sensitive to the number of samples. However, there is no work to fully study the problem of sample location selection w.r.t. DAIM in geo-social networks. To do this, we first formalize the problem under a reasonable assumption that a promoted location always adheres to the distribution of users. Then, we propose an efficient location sampling approach based on the heuristic anchor point selection and facility allocation techniques. Our experimental results on two real datasets demonstrate that our approach can improve the online and offline efficiency of DAIM approach like [9] by orders of magnitude.
History
Event
Database Systems. Conference (23rd : 2018 : Gold Coast, Qld.)Volume
10827Series
Database Systems ConferencePagination
355 - 371Publisher
SpringerLocation
Gold Coast, Qld.Place of publication
Cham, SwitzerlandPublisher DOI
Start date
2018-05-21End date
2018-05-24ISBN-13
978-3-319-91452-7Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2018, Springer International Publishing AG, part of Springer NatureEditor/Contributor(s)
J Pei, Y Manolopoulos, S Sadiq, J LiTitle of proceedings
DASFAA 2018 : Proceedings of the 23rd International Conference on Database Systems for Advanced ApplicationsUsage metrics
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