research-article

Location-Based Influence Maximization in Social Networks

Authors:

Junzhou LuoAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1211 - 1220

https://doi.org/10.1145/2806416.2806462

Published: 17 October 2015 Publication History

Abstract

In this paper, we aim at the product promotion in O2O model and carry out the research of location-based influence maximization on the platform of LBSN. As offline consuming behavior exists under the O2O environment, the traditional online influence diffusion model could not describe the product acceptance accurately. Moreover, the existing researches of influence maximization tend to only concern on the online network of relationships but rarely take the offline part into consideration. This paper introduces the location property into the influence maximization to accord with the characteristic of O2O model. Firstly, we propose an improved influence diffusion model called TP Model which could accurately describe the process of accepting products under the O2O environment. Meanwhile, the definition of location-based influence maximization is presented. Then the user mobility pattern is analyzed and the calculation method of offline probability is designed. Considering the influence ability, a location-based influence maximization algorithm named TPH is proposed. Experiments prove TPH algorithm has general advantage. Finally, focusing on the performance of TPH algorithm under special circumstances, MR algorithm is designed as complement and experiments also verify its high effectiveness.

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Cited By

Deligkas ADöring MEiben EGoldsmith TSkretas G(2024)Being an influencer is hard: The complexity of influence maximization in temporal graphs with a fixed sourceInformation and Computation10.1016/j.ic.2024.105171(105171)Online publication date: May-2024
https://doi.org/10.1016/j.ic.2024.105171
Deligkas AEiben EGoldsmith TSkretas GAgmon NAn BRicci AYeoh W(2023)Being an Influencer is Hard: The Complexity of Influence Maximization in Temporal Graphs with a Fixed SourceProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598898(2222-2230)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598898
Zareie ASakellariou R(2023)Influence maximization in social networks: a survey of behaviour-aware methodsSocial Network Analysis and Mining10.1007/s13278-023-01078-913:1Online publication date: 25-Apr-2023
https://doi.org/10.1007/s13278-023-01078-9
Show More Cited By

Index Terms

Location-Based Influence Maximization in Social Networks
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

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Publication History

Published: 17 October 2015

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National Natural Science Foundation of China

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Deligkas ADöring MEiben EGoldsmith TSkretas G(2024)Being an influencer is hard: The complexity of influence maximization in temporal graphs with a fixed sourceInformation and Computation10.1016/j.ic.2024.105171(105171)Online publication date: May-2024
https://doi.org/10.1016/j.ic.2024.105171
Deligkas AEiben EGoldsmith TSkretas GAgmon NAn BRicci AYeoh W(2023)Being an Influencer is Hard: The Complexity of Influence Maximization in Temporal Graphs with a Fixed SourceProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3598898(2222-2230)Online publication date: 30-May-2023
https://dl.acm.org/doi/10.5555/3545946.3598898
Zareie ASakellariou R(2023)Influence maximization in social networks: a survey of behaviour-aware methodsSocial Network Analysis and Mining10.1007/s13278-023-01078-913:1Online publication date: 25-Apr-2023
https://doi.org/10.1007/s13278-023-01078-9
Hai-Jew S(2022)Self-Presenting Virtually for Remote Social InfluencePractical Peer-to-Peer Teaching and Learning on the Social Web10.4018/978-1-7998-6496-7.ch013(407-461)Online publication date: 2022
https://doi.org/10.4018/978-1-7998-6496-7.ch013
Chen XZhao YLiu GSun RZhou XZheng K(2022)Efficient Similarity-Aware Influence Maximization in Geo-Social NetworkIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.304578334:10(4767-4780)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TKDE.2020.3045783
N RGupta YSukhija S(2022)Source Aware Budgeted Information Maximization2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM55673.2022.10068591(186-193)Online publication date: 10-Nov-2022
https://doi.org/10.1109/ASONAM55673.2022.10068591
Singh SSrivastva DVerma MSingh J(2022)Influence maximization frameworks, performance, challenges and directions on social networkJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2021.08.00934:9(7570-7603)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1016/j.jksuci.2021.08.009
Ye YChen YHan W(2022)Influence maximization in social networks: Theories, methods and challengesArray10.1016/j.array.2022.10026416(100264)Online publication date: Dec-2022
https://doi.org/10.1016/j.array.2022.100264
Chen XDeng LZhao YZhou XZheng K(2021)Community-based influence maximization in location-based social networkWorld Wide Web10.1007/s11280-021-00935-x24:6(1903-1928)Online publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1007/s11280-021-00935-x
Zhang KZhou JTao DKarras PLi QXiong HGupta RLiu YShah MRajan STang JPrakash B(2020)Geodemographic Influence MaximizationProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403327(2764-2774)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403327
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