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Fuzzy Influence Maximization in Social Networks

Authors:

Rizos SakellariouAuthors Info & Claims

ACM Transactions on the Web, Volume 18, Issue 3

Article No.: 32, Pages 1 - 28

https://doi.org/10.1145/3650179

Published: 15 April 2024 Publication History

Abstract

Influence maximization is a fundamental problem in social network analysis. This problem refers to the identification of a set of influential users as initial spreaders to maximize the spread of a message in a network. When such a message is spread, some users may be influenced by it. A common assumption of existing work is that the impact of a message is essentially binary: A user is either influenced (activated) or not influenced (non-activated). However, how strongly a user is influenced by a message may play an important role in this user’s attempt to influence subsequent users and spread the message further; existing methods may fail to model accurately the spreading process and identify influential users. In this article, we propose a novel approach to model a social network as a fuzzy graph where a fuzzy variable is used to represent the extent to which a user is influenced by a message (user’s activation level). By extending a diffusion model to simulate the spreading process in such a fuzzy graph, we conceptually formulate the fuzzy influence maximization problem for which three methods are proposed to identify influential users. Experimental results demonstrate the accuracy of the proposed methods in determining influential users in social networks.

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Cited By

Achour ORomdhane L(2025)A theoretical review on multiplex influence maximization models: Theories, methods, challenges, and future directionsExpert Systems with Applications10.1016/j.eswa.2024.125990266(125990)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125990
Ittan MElayidom SMathew M(2024)The Social Media Influence JourneyEnhancing Communication and Decision-Making With AI10.4018/979-8-3693-9246-1.ch003(65-98)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-9246-1.ch003
Zhang CCheng WLi FWang X(2024)Hypergraph-Based Influence Maximization in Online Social NetworksMathematics10.3390/math1217276912:17(2769)Online publication date: 7-Sep-2024
https://doi.org/10.3390/math12172769
Show More Cited By

Index Terms

Fuzzy Influence Maximization in Social Networks
1. Information systems
  1. World Wide Web
    1. Web applications
      1. Social networks

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Published In

cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 18, Issue 3

August 2024

254 pages

EISSN:1559-114X

DOI:10.1145/3613679

Editor:
Ryen White
Microsoft Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 April 2024

Online AM: 01 March 2024

Accepted: 18 December 2023

Revised: 20 September 2023

Received: 15 June 2022

Published in TWEB Volume 18, Issue 3

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Cited By

Achour ORomdhane L(2025)A theoretical review on multiplex influence maximization models: Theories, methods, challenges, and future directionsExpert Systems with Applications10.1016/j.eswa.2024.125990266(125990)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125990
Ittan MElayidom SMathew M(2024)The Social Media Influence JourneyEnhancing Communication and Decision-Making With AI10.4018/979-8-3693-9246-1.ch003(65-98)Online publication date: 27-Sep-2024
https://doi.org/10.4018/979-8-3693-9246-1.ch003
Zhang CCheng WLi FWang X(2024)Hypergraph-Based Influence Maximization in Online Social NetworksMathematics10.3390/math1217276912:17(2769)Online publication date: 7-Sep-2024
https://doi.org/10.3390/math12172769
Biswas TAbbasi AChakrabortty R(2024)A cost-effective seed selection model for multi-constraint influence maximization in social networksDecision Analytics Journal10.1016/j.dajour.2024.10047411(100474)Online publication date: Jun-2024
https://doi.org/10.1016/j.dajour.2024.100474

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