research-article

Influence Maximization in Multi-Relational Social Networks

Authors:

Leyu LinAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4193 - 4202

https://doi.org/10.1145/3459637.3481928

Published: 30 October 2021 Publication History

Abstract

Influence maximization (IM) is a classic problem, which aims to find a set of k users (called seed set) in a social network such that the expected number of users influenced by the seed users is maximized. Existing IM algorithms mainly focus on one-by-one influence diffusion among users with friendships. However, in addition to 1-to-1 friendships, 1-to-N group relations usually exist in real social platforms, which are seldom fully exploited by conventional methods.

In this paper, with the real-world datasets in WeChat, the largest online social platform in China, we first study the IM problem in multi-relational social networks consisting of friendships and group relations, and propose a novel Generate&Extend framework to find influential seed users for product promotion. Specifically, to achieve a trade-off between effectiveness and efficiency, we present a truncated meta-seed generator to select a small number of users, which are influential with consideration of both friendships and group relations. More importantly, a structural seed extender is put forward to extend the meta-seed set, so as to encode the differentiated propagation structures between friendships and group relations. Extensive online/offline experiments on three real-world datasets demonstrate that Generate&Extend significantly outperforms the state of the arts. Our Generate&Extend has been deployed at WeChat for mini-program promoting, and severing more than 200 million users.

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Cited By

Sun YWu JSong NLin TLi LLi D(2025)Deep reinforcement learning-based influence maximization for heterogeneous hypergraphsPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2025.130361660(130361)Online publication date: Feb-2025
https://doi.org/10.1016/j.physa.2025.130361
Zhang HZhang DWan YPan RKou G(2025)Multiplex network influence maximization based on representation learning methodApplied Soft Computing10.1016/j.asoc.2025.112956(112956)Online publication date: Mar-2025
https://doi.org/10.1016/j.asoc.2025.112956
Guo CLi WWang JYu XLiu XLuvembe AWang CJin Q(2024)Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation fieldKnowledge-Based Systems10.1016/j.knosys.2024.111580291:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111580
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Index Terms

Influence Maximization in Multi-Relational Social Networks
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Mobile information processing systems
2. Social and professional topics
  1. User characteristics

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Published In

cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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Cited By

Sun YWu JSong NLin TLi LLi D(2025)Deep reinforcement learning-based influence maximization for heterogeneous hypergraphsPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2025.130361660(130361)Online publication date: Feb-2025
https://doi.org/10.1016/j.physa.2025.130361
Zhang HZhang DWan YPan RKou G(2025)Multiplex network influence maximization based on representation learning methodApplied Soft Computing10.1016/j.asoc.2025.112956(112956)Online publication date: Mar-2025
https://doi.org/10.1016/j.asoc.2025.112956
Guo CLi WWang JYu XLiu XLuvembe AWang CJin Q(2024)Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation fieldKnowledge-Based Systems10.1016/j.knosys.2024.111580291:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.knosys.2024.111580
Li LHuang ZCui W(2024)Influence maximization in blockchain social networks: a heterogeneous LDAG approachWorld Wide Web10.1007/s11280-024-01308-w27:6Online publication date: 30-Sep-2024
https://doi.org/10.1007/s11280-024-01308-w
Yang MSeklouli AZhang HRen LYu XOuzrout Y(2023)A Review on Influence Dissemination in Social Networks2023 International Conference on Computer Applications Technology (CCAT)10.1109/CCAT59108.2023.00025(97-103)Online publication date: 15-Sep-2023
https://doi.org/10.1109/CCAT59108.2023.00025
Arazzi MNicolazzo SNocera AZippo M(2023)The importance of the language for the evolution of online communitiesExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119847222:COnline publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119847
K. VChowdary C(2023)K＋＋ ShellComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2023.109916234:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.comnet.2023.109916
Ghafouri SKhasteh SAzarkasb S(2023)Influence maximization (IM) in complex networks with limited visibility using statistical methodsThe Journal of Supercomputing10.1007/s11227-023-05695-180:5(6809-6854)Online publication date: 30-Oct-2023
https://dl.acm.org/doi/10.1007/s11227-023-05695-1
Chen BJiang WChen YChen LWang RHan SLin JZhang Y(2022)Influence blocking maximization on networks: Models, methods and applicationsPhysics Reports10.1016/j.physrep.2022.05.003976(1-54)Online publication date: Sep-2022
https://doi.org/10.1016/j.physrep.2022.05.003

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