Influence Maximization with Novelty Decay in Social Networks

Shanshan Feng, Xufeng Chen, Gao Cong, Yifeng Zeng, Yeow Meng Chee, Yanping Xiang

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    Abstract

    Influence maximization problem is to find a set of seed nodes in a social network such that their influence spread is maximized under certain propagation models. A few algorithms have been proposed for solving this problem. However, they have not considered the impact of novelty decay on influence propagation, i.e., repeated exposures will have diminishing influence on users. In this paper, we consider the problem of influence maximization with novelty decay (IMND). We investigate the effect of novelty decay on influence propagation on real-life datasets and formulate the IMND problem. We further analyze the problem properties and propose an influence estimation technique. We demonstrate the performance of our algorithms on four social networks.
    Original languageEnglish
    Publication statusPublished - 2014
    Event28th AAAI Conference on Artificial Intelligence - Québec City, Canada
    Duration: 27 Jul 201431 Jul 2014
    https://www.aaai.org/ocs/index.php/AAAI/AAAI14/index

    Conference

    Conference28th AAAI Conference on Artificial Intelligence
    Abbreviated titleAAAI 2014
    CountryCanada
    CityQuébec City
    Period27/07/1431/07/14
    Internet address

    Bibliographical note

    AAAI authors are granted back the right to use their own papers for noncommercial uses. Publisher advice [Recieved: 25/01/2016]

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  • Cite this

    Feng, S., Chen, X., Cong, G., Zeng, Y., Chee, Y. M., & Xiang, Y. (2014). Influence Maximization with Novelty Decay in Social Networks. Paper presented at 28th AAAI Conference on Artificial Intelligence, Québec City, Canada.