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Overexposure-Aware Influence Maximization

Published: 06 October 2020 Publication History

Abstract

Viral marketing campaigns are often negatively affected by overexposure. Overexposure occurs when users become less likely to favor a promoted product after receiving information about the product from too large a fraction of their friends. Yet, existing influence diffusion models do not take overexposure into account, effectively overestimating the number of users who favor the product and diffuse information about it. In this work, we propose the first influence diffusion model that captures overexposure. In our model, Latency Aware Independent Cascade Model with Overexposure (LAICO), the activation probability of a node representing a user is multiplied (discounted) by an overexposure score, which is calculated based on the ratio between the estimated and the maximum possible number of attempts performed to activate the node. We also study the influence maximization problem under LAICO. Since the spread function in LAICO is non-submodular, algorithms for submodular maximization are not appropriate to address the problem. Therefore, we develop an approximation algorithm that exploits monotone submodular upper and lower bound functions of spread, and a heuristic that aims to maximize a proxy function of spread iteratively. Our experiments show the effectiveness and efficiency of our algorithms.

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  • (2025)Target Influence Maximization Against Overexposure Under Threshold-Dependent Model in Online Social NetworksComputing and Combinatorics10.1007/978-981-96-1093-8_10(116-127)Online publication date: 20-Feb-2025
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    cover image ACM Transactions on Internet Technology
    ACM Transactions on Internet Technology  Volume 20, Issue 4
    November 2020
    391 pages
    ISSN:1533-5399
    EISSN:1557-6051
    DOI:10.1145/3427795
    • Editor:
    • Ling Liu
    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 ACM 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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    Publication History

    Published: 06 October 2020
    Accepted: 01 June 2020
    Revised: 01 April 2020
    Received: 01 February 2020
    Published in TOIT Volume 20, Issue 4

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    Author Tags

    1. Influence maximization
    2. influence diffusion
    3. social networks

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    Cited By

    View all
    • (2025)Target Influence Maximization Against Overexposure Under Threshold-Dependent Model in Online Social NetworksComputing and Combinatorics10.1007/978-981-96-1093-8_10(116-127)Online publication date: 20-Feb-2025
    • (2024)Relieving Overexposure in Information Diffusion Through a Budget Multi-stage AllocationACM Transactions on Internet Technology10.1145/370853725:1(1-26)Online publication date: 17-Dec-2024
    • (2023)Equilibrium of individual concern-critical influence maximization in virtual and real blending networkInformation Sciences10.1016/j.ins.2023.119646648(119646)Online publication date: Nov-2023
    • (2022)Cascades and Overexposure in Social NetworksProceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems10.5555/3535850.3535923(642-650)Online publication date: 9-May-2022
    • (2022)Chinese Contemporary Music Diffusion Strategy Based on Public Opinion MaximizationMobile Information Systems10.1155/2022/79294492022Online publication date: 1-Jan-2022
    • (2022)Reconfiguration Problems on Submodular FunctionsProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498382(764-774)Online publication date: 11-Feb-2022

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