research-article

Online Processing Algorithms for Influence Maximization

Authors:

Junsong YuanAuthors Info & Claims

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

Pages 991 - 1005

https://doi.org/10.1145/3183713.3183749

Published: 27 May 2018 Publication History

Abstract

Influence maximization is a classic and extensively studied problem with important applications in viral marketing. Existing algorithms for influence maximization, however, mostly focus on offline processing, in the sense that they do not provide any output to the user until the final answer is derived, and that the user is not allowed to terminate the algorithm early to trade the quality of solution for efficiency. Such lack of interactiveness and flexibility leads to poor user experience, especially when the algorithm incurs long running time.

To address the above problem, this paper studies algorithms for online processing of influence maximization (OPIM), where the user can pause the algorithm at any time and ask for a solution (to the influence maximization problem) and its approximation guarantee, and can resume the algorithm to let it improve the quality of solution by giving it more time to run. (This interactive paradigm is similar in spirit to online query processing in database systems.) We show that the only existing algorithm for OPIM is vastly ineffective in practice, and that adopting existing influence maximization methods for OPIM yields unsatisfactory results. Motivated by this, we propose a new algorithm for OPIM with both superior empirical effectiveness and strong theoretical guarantees, and we show that it can also be extended to handle conventional influence maximization. Extensive experiments on real data demonstrate that our solutions outperform the state of the art for both OPIM and conventional influence maximization.

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Chen XSong YTang JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Link Recommendation to Augment Influence Diffusion with Provable GuaranteesProceedings of the ACM Web Conference 202410.1145/3589334.3645521(2509-2518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645521
Biswas TAbbasi AChakrabortty R(2024)Generalized hop‐based approaches for identifying influential nodes in social networksExpert Systems10.1111/exsy.13649Online publication date: 4-Jun-2024
https://doi.org/10.1111/exsy.13649
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Index Terms

Online Processing Algorithms for Influence Maximization
1. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Online advertising
      1. Social advertising
    2. Web applications
      1. Social networks
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Mixed discrete-continuous optimization
        Submodular optimization and polymatroids
  2. Models of computation
    1. Probabilistic computation

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cover image ACM Conferences

SIGMOD '18: Proceedings of the 2018 International Conference on Management of Data

May 2018

1874 pages

ISBN:9781450347037

DOI:10.1145/3183713

General Chairs:
Gautam Das
University of Texas at Arlington, USA
,
Christopher Jermaine
Rice University, USA
,
Philip Bernstein
Microsoft Research, USA

Copyright © 2018 ACM.

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Published: 27 May 2018

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Singapore Ministry of Education
National University of Singapore

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SIGMOD/PODS '18: International Conference on Management of Data

June 10 - 15, 2018

TX, Houston, USA

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SIGMOD '18 Paper Acceptance Rate 90 of 461 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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https://dl.acm.org/doi/10.14778/3681954.3682029
Chen XSong YTang JChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Link Recommendation to Augment Influence Diffusion with Provable GuaranteesProceedings of the ACM Web Conference 202410.1145/3589334.3645521(2509-2518)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645521
Biswas TAbbasi AChakrabortty R(2024)Generalized hop‐based approaches for identifying influential nodes in social networksExpert Systems10.1111/exsy.13649Online publication date: 4-Jun-2024
https://doi.org/10.1111/exsy.13649
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Guo JNi QWu WDu D(2024)Composite Community-Aware Diversified Influence Maximization With Efficient ApproximationIEEE/ACM Transactions on Networking10.1109/TNET.2023.332187032:2(1584-1599)Online publication date: Apr-2024
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Zhang YGuo JYang WWu W(2024)Supplementary Influence Maximization Problem in Social NetworksIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.323443711:1(986-996)Online publication date: Feb-2024
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Zhang NYe YLian XChen M(2024)Top-$L$ Most Influential Community Detection Over Social Networks2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.10639540(5767-5779)Online publication date: 13-May-2024
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