research-article

Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation

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Wei ChenAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 164, Pages 1 - 26

https://doi.org/10.1145/3589309

Published: 20 June 2023 Publication History

Abstract

In this paper, we present an algorithmic study on how to surpass competitors in popularity by strategic promotions in social networks. We first propose a novel model, in which we integrate the Preferential Attachment (PA) model for popularity growth with the Independent Cascade (IC) model for influence propagation in social networks called PA-IC model. In PA-IC, a popular item and a novice item grab shares of popularity from the natural popularity growth via the PA model, while the novice item tries to gain extra popularity via influence cascade in a social network. The popularity ratio is defined as the ratio of the popularity measure between the novice item and the popular item. We formulate Popularity Ratio Maximization (PRM) as the problem of selecting seeds in multiple rounds to maximize the popularity ratio in the end. We analyze the popularity ratio and show that it is monotone but not submodular. To provide an effective solution, we devise a surrogate objective function and show that empirically it is very close to the original objective function while theoretically, it is monotone and submodular. We design two efficient algorithms, one for the overlapping influence and non-overlapping seeds (across rounds) setting and the other for the non-overlapping influence and overlapping seed setting, and further discuss how to deal with other models and problem variants. Our empirical evaluation further demonstrates that our proposed method consistently achieves the best popularity promotion compared to other methods. Our theoretical and empirical analyses shed light on the interplay between influence maximization and preferential attachment in social networks.

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Cited By

Liu HLuo S(2024)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRankProceedings of the VLDB Endowment10.14778/3665844.366585517:9(2255-2268)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665855
Sun HZhang ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficient Computation for Diagonal of Forest Matrix via Variance-Reduced Forest SamplingProceedings of the ACM Web Conference 202410.1145/3589334.3645578(792-802)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645578
Zhang DRong ZXue CLi G(2024)SimREInformation Sciences: an International Journal10.1016/j.ins.2023.120069661:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.ins.2023.120069

Index Terms

Popularity Ratio Maximization: Surpassing Competitors through Influence Propagation
1. Information systems
  1. Information systems applications

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Copyright © 2023 ACM.

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Publication History

Published: 20 June 2023

Published in PACMMOD Volume 1, Issue 2

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Cited By

Liu HLuo S(2024)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRankProceedings of the VLDB Endowment10.14778/3665844.366585517:9(2255-2268)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.14778/3665844.3665855
Sun HZhang ZChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Efficient Computation for Diagonal of Forest Matrix via Variance-Reduced Forest SamplingProceedings of the ACM Web Conference 202410.1145/3589334.3645578(792-802)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645578
Zhang DRong ZXue CLi G(2024)SimREInformation Sciences: an International Journal10.1016/j.ins.2023.120069661:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.ins.2023.120069

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