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Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application

Published: 08 May 2017 Publication History

Abstract

This paper focuses on a topic that is insufficiently addressed in the literature, i.e., challenges faced in transitioning agents from an emerging phase in the lab, to a deployed application in the field. Specifically, we focus on challenges faced in transitioning HEALER and DOSIM, two agents for social influence maximization, which assist service providers in maximizing HIV awareness in real-world homeless-youth social networks. These agents recommend key "seed" nodes in social networks, i.e., homeless youth who would maximize HIV awareness in their real-world social network. While prior research on these agents published promising simulation results from the lab, this paper illustrates that transitioning these agents from the lab into the real-world is not straightforward, and outlines three major lessons. First, it is important to conduct real-world pilot tests; indeed, due to the health-critical nature of the domain and complex influence spread models used by these agents, it is important to conduct field tests to ensure the real-world usability and effectiveness of these agents. We present results from three real-world pilot studies, involving 173 homeless youth in an American city. These are the first such pilot studies which provide head-to-head comparison of different agents for social influence maximization, including a comparison with a baseline approach. Second, we present analyses of these real-world results, illustrating the strengths and weaknesses of different influence maximization approaches we compare. Third, we present research and deployment challenges revealed in conducting these pilot tests, and propose solutions to address them. These challenges and proposed solutions are instructive in assisting the transition of agents focused on social influence maximization from the emerging to the deployed application phase.

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Cited By

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  • (2023)The Influence Maximisation GameProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599172(3059-3061)Online publication date: 30-May-2023
  • (2023)Differentially Private Network Data Collection for Influence MaximizationProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599081(2795-2797)Online publication date: 30-May-2023
  • (2021)Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 PandemicProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464047(790-798)Online publication date: 3-May-2021
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  1. Influence Maximization in the Field: The Arduous Journey from Emerging to Deployed Application

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    Published In

    cover image ACM Other conferences
    AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems
    May 2017
    1914 pages

    Sponsors

    • IFAAMAS

    In-Cooperation

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    International Foundation for Autonomous Agents and Multiagent Systems

    Richland, SC

    Publication History

    Published: 08 May 2017

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    Author Tags

    1. POMDP
    2. influence maximization
    3. innovative applications
    4. real-world deployment
    5. social networks

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    • Research-article

    Funding Sources

    • MURI
    • NIMH Grant

    Acceptance Rates

    AAMAS '17 Paper Acceptance Rate 127 of 457 submissions, 28%;
    Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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    Cited By

    View all
    • (2023)The Influence Maximisation GameProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599172(3059-3061)Online publication date: 30-May-2023
    • (2023)Differentially Private Network Data Collection for Influence MaximizationProceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems10.5555/3545946.3599081(2795-2797)Online publication date: 30-May-2023
    • (2021)Let the DOCTOR Decide Whom to Test: Adaptive Testing Strategies to Tackle the COVID-19 PandemicProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464047(790-798)Online publication date: 3-May-2021
    • (2020)Identifying Homeless Youth At-Risk of Substance Use DisorderProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403360(3092-3100)Online publication date: 23-Aug-2020
    • (2019)Computational sustainabilityCommunications of the ACM10.1145/333939962:9(56-65)Online publication date: 21-Aug-2019
    • (2019)Optimizing peer referrals for public awareness using contextual banditsProceedings of the 2nd ACM SIGCAS Conference on Computing and Sustainable Societies10.1145/3314344.3332497(74-85)Online publication date: 3-Jul-2019
    • (2018)Algorithmic social interventionProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304857(5793-5794)Online publication date: 13-Jul-2018
    • (2018)Bridging the gap between theory and practice in influence maximizationProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304652.3304774(5399-5403)Online publication date: 13-Jul-2018
    • (2018)Activating theProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237941(1631-1639)Online publication date: 9-Jul-2018
    • (2018)Please be an Influencer?Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237912(1423-1431)Online publication date: 9-Jul-2018
    • Show More Cited By

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