Sample location selection for efficient distance-aware influence maximization in geo-social networks

Ming Zhong, Qian Zeng, Yuanyuan Zhu, Jianxin Li, Tieyun Qian

Research output: Chapter in Book/Conference paperConference paper

6 Citations (Scopus)

Abstract

In 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.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications
Subtitle of host publication23rd International Conference, DASFAA 2018 Gold Coast, QLD, Australia, May 21–24, 2018 Proceedings, Part I
Place of PublicationCham, Switzerland
PublisherSpringer-Verlag London Ltd.
Pages355-371
Number of pages17
ISBN (Electronic)9783319914527
ISBN (Print)9783319914510
DOIs
Publication statusPublished - 21 May 2018
Event23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018 - Gold Coast, Australia
Duration: 21 May 201824 May 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10827 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
CountryAustralia
CityGold Coast
Period21/05/1824/05/18

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    Zhong, M., Zeng, Q., Zhu, Y., Li, J., & Qian, T. (2018). Sample location selection for efficient distance-aware influence maximization in geo-social networks. In Database Systems for Advanced Applications: 23rd International Conference, DASFAA 2018 Gold Coast, QLD, Australia, May 21–24, 2018 Proceedings, Part I (pp. 355-371). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10827 LNCS). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-91452-7_24