Mining of location-based social networks for spatio-temporal social influence

Yu Ting Wen*, Yi Yuan Fan, Wen-Chih Peng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Following the advent of location-based social networks (LBSNs), location-aware services have attracted considerable attention among researchers. Research has shown that the social network is regarded as one of the strongest influences shaping individual attitudes and behaviors. This paper targets the mining of location-based social influences hidden in LBSNs. In other words, we sought to determine whether an individual’s check-in behavior is influenced by friends’ check-ins. Check-in data includes positional information; therefore, we refer to this type of influence as spatiotemporal social influences. This study proposes a framework for spatiotemporal social influence mining (ST-SIM) to identify users with the greatest influence on individuals (i.e., close friends and travel experts) from an LBSN and estimate the strength of these social connections. Explicitly, the proposed framework is able to infer a list of influential users of an individual under given conditions based on travel distance, visiting time or POI categories. We developed a diffusion-based mechanism for modeling the propagation of influence over time. Our experiment results demonstrate that the ST-SIM framework outperforms state-of-the-art methods in terms of accuracy and reliability, and is applicable in domains ranging from marketing to intelligence analysis.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783319574530
StatePublished - 1 Jan 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 23 May 201726 May 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
CountryKorea, Republic of


  • Influence propagation
  • Location-based social network

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