Influence Propagation in Competitive Scenario: Winning with Least Amount of Investment in Separated Threshold Model

Anisha Mazumder, Arunabha Sen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    In a market environment, there often are multiple vendors offering similar products or services. It has been observed that individuals' decisions to adopt a product or service are influenced by the recommendations of their friends and acquaintances. Consequently, in the last few years there has been considerable interest in the research community to study the dynamics of influence propagation in social networks in competitive settings. The goal of these studies is often to identify the key individuals in a social network, whose recommendations have significant impact on adoption of a product or service by the members of that community. Using Separated Threshold Model (SepT) [1] of influence propagation, in this paper we study a problem of similar vein, where the goal of the two vendors (players) is to win the competition by having a market share that is larger than its competitor. In our model, the first player has already identified a set of key influencers when the second player enters the market. The goal of the second player is to have a larger market share, but wants to achieve the goal with least amount of investment, i.e., by incentivizing the fewest number of key individuals (influencers) in the social network. The problem is NP-hard. We provide an approximation algorithm with O(log n) bound. Detailed experimentations have been conducted to evaluate the efficacy of our algorithm. Moreover, we present an equivalent random process for the SepT model which facilitates analysis of competitive influence propagation under this model.

    Original languageEnglish (US)
    Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages5221-5226
    Number of pages6
    ISBN (Electronic)9781538613955
    DOIs
    StatePublished - Jan 18 2019
    Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
    Duration: Dec 17 2018Dec 19 2018

    Publication series

    NameProceedings of the IEEE Conference on Decision and Control
    Volume2018-December
    ISSN (Print)0743-1546

    Conference

    Conference57th IEEE Conference on Decision and Control, CDC 2018
    CountryUnited States
    CityMiami
    Period12/17/1812/19/18

    Fingerprint

    Threshold Model
    Propagation
    Social Networks
    Scenarios
    Recommendations
    Veins
    Random process
    Experimentation
    Efficacy
    Approximation Algorithms
    Approximation algorithms
    Random processes
    NP-complete problem
    Model
    Computational complexity
    Influence
    Market
    Evaluate
    Community

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Modeling and Simulation
    • Control and Optimization

    Cite this

    Mazumder, A., & Sen, A. (2019). Influence Propagation in Competitive Scenario: Winning with Least Amount of Investment in Separated Threshold Model. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 5221-5226). [8618661] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8618661

    Influence Propagation in Competitive Scenario : Winning with Least Amount of Investment in Separated Threshold Model. / Mazumder, Anisha; Sen, Arunabha.

    2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5221-5226 8618661 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Mazumder, A & Sen, A 2019, Influence Propagation in Competitive Scenario: Winning with Least Amount of Investment in Separated Threshold Model. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8618661, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 5221-5226, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8618661
    Mazumder A, Sen A. Influence Propagation in Competitive Scenario: Winning with Least Amount of Investment in Separated Threshold Model. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5221-5226. 8618661. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2018.8618661
    Mazumder, Anisha ; Sen, Arunabha. / Influence Propagation in Competitive Scenario : Winning with Least Amount of Investment in Separated Threshold Model. 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5221-5226 (Proceedings of the IEEE Conference on Decision and Control).
    @inproceedings{6148b5ede043450ab81a78ca5172476b,
    title = "Influence Propagation in Competitive Scenario: Winning with Least Amount of Investment in Separated Threshold Model",
    abstract = "In a market environment, there often are multiple vendors offering similar products or services. It has been observed that individuals' decisions to adopt a product or service are influenced by the recommendations of their friends and acquaintances. Consequently, in the last few years there has been considerable interest in the research community to study the dynamics of influence propagation in social networks in competitive settings. The goal of these studies is often to identify the key individuals in a social network, whose recommendations have significant impact on adoption of a product or service by the members of that community. Using Separated Threshold Model (SepT) [1] of influence propagation, in this paper we study a problem of similar vein, where the goal of the two vendors (players) is to win the competition by having a market share that is larger than its competitor. In our model, the first player has already identified a set of key influencers when the second player enters the market. The goal of the second player is to have a larger market share, but wants to achieve the goal with least amount of investment, i.e., by incentivizing the fewest number of key individuals (influencers) in the social network. The problem is NP-hard. We provide an approximation algorithm with O(log n) bound. Detailed experimentations have been conducted to evaluate the efficacy of our algorithm. Moreover, we present an equivalent random process for the SepT model which facilitates analysis of competitive influence propagation under this model.",
    author = "Anisha Mazumder and Arunabha Sen",
    year = "2019",
    month = "1",
    day = "18",
    doi = "10.1109/CDC.2018.8618661",
    language = "English (US)",
    series = "Proceedings of the IEEE Conference on Decision and Control",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    pages = "5221--5226",
    booktitle = "2018 IEEE Conference on Decision and Control, CDC 2018",

    }

    TY - GEN

    T1 - Influence Propagation in Competitive Scenario

    T2 - Winning with Least Amount of Investment in Separated Threshold Model

    AU - Mazumder, Anisha

    AU - Sen, Arunabha

    PY - 2019/1/18

    Y1 - 2019/1/18

    N2 - In a market environment, there often are multiple vendors offering similar products or services. It has been observed that individuals' decisions to adopt a product or service are influenced by the recommendations of their friends and acquaintances. Consequently, in the last few years there has been considerable interest in the research community to study the dynamics of influence propagation in social networks in competitive settings. The goal of these studies is often to identify the key individuals in a social network, whose recommendations have significant impact on adoption of a product or service by the members of that community. Using Separated Threshold Model (SepT) [1] of influence propagation, in this paper we study a problem of similar vein, where the goal of the two vendors (players) is to win the competition by having a market share that is larger than its competitor. In our model, the first player has already identified a set of key influencers when the second player enters the market. The goal of the second player is to have a larger market share, but wants to achieve the goal with least amount of investment, i.e., by incentivizing the fewest number of key individuals (influencers) in the social network. The problem is NP-hard. We provide an approximation algorithm with O(log n) bound. Detailed experimentations have been conducted to evaluate the efficacy of our algorithm. Moreover, we present an equivalent random process for the SepT model which facilitates analysis of competitive influence propagation under this model.

    AB - In a market environment, there often are multiple vendors offering similar products or services. It has been observed that individuals' decisions to adopt a product or service are influenced by the recommendations of their friends and acquaintances. Consequently, in the last few years there has been considerable interest in the research community to study the dynamics of influence propagation in social networks in competitive settings. The goal of these studies is often to identify the key individuals in a social network, whose recommendations have significant impact on adoption of a product or service by the members of that community. Using Separated Threshold Model (SepT) [1] of influence propagation, in this paper we study a problem of similar vein, where the goal of the two vendors (players) is to win the competition by having a market share that is larger than its competitor. In our model, the first player has already identified a set of key influencers when the second player enters the market. The goal of the second player is to have a larger market share, but wants to achieve the goal with least amount of investment, i.e., by incentivizing the fewest number of key individuals (influencers) in the social network. The problem is NP-hard. We provide an approximation algorithm with O(log n) bound. Detailed experimentations have been conducted to evaluate the efficacy of our algorithm. Moreover, we present an equivalent random process for the SepT model which facilitates analysis of competitive influence propagation under this model.

    UR - http://www.scopus.com/inward/record.url?scp=85062168303&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85062168303&partnerID=8YFLogxK

    U2 - 10.1109/CDC.2018.8618661

    DO - 10.1109/CDC.2018.8618661

    M3 - Conference contribution

    T3 - Proceedings of the IEEE Conference on Decision and Control

    SP - 5221

    EP - 5226

    BT - 2018 IEEE Conference on Decision and Control, CDC 2018

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -