Information diffusion in heterogeneous groups

Jennifer M. Larson

    Research output: Contribution to journalArticle

    Abstract

    Standard approaches to the study of information diffusion draw on analogies to the transmission of diseases or computer viruses, and find that adding more random ties to a network increases the speed of information propagation through it. However, a person sharing information in a social network differs from a computer transmitting a virus in two important respects: a person may not have the opportunity to pass the information to every tie, and may be unwilling to pass the information to certain ties even when presented with the opportunity. Accounting for these two features reveals that, while additional random ties allow information to jump to distant regions of a network, they also change the composition of network neighborhoods. When the latter increases the proportion of neighbors to whom people are less willing to pass information, the result can be a net decrease in diffusion. I show that this is the case in heterogeneous, homophilous networks: the addition of random ties strictly impedes information dissemination, and the impediment is increasing in both original homophily and the number of new ties.

    Original languageEnglish (US)
    Pages (from-to)449-458
    Number of pages10
    JournalStudies in Computational Intelligence
    Volume693
    DOIs
    StatePublished - 2017

    Fingerprint

    Computer viruses
    Information dissemination
    Heterogeneous networks
    Chemical analysis

    ASJC Scopus subject areas

    • Artificial Intelligence

    Cite this

    Information diffusion in heterogeneous groups. / Larson, Jennifer M.

    In: Studies in Computational Intelligence, Vol. 693, 2017, p. 449-458.

    Research output: Contribution to journalArticle

    Larson, Jennifer M. / Information diffusion in heterogeneous groups. In: Studies in Computational Intelligence. 2017 ; Vol. 693. pp. 449-458.
    @article{eefd5207bbc94a269e62cbfd02b25a4e,
    title = "Information diffusion in heterogeneous groups",
    abstract = "Standard approaches to the study of information diffusion draw on analogies to the transmission of diseases or computer viruses, and find that adding more random ties to a network increases the speed of information propagation through it. However, a person sharing information in a social network differs from a computer transmitting a virus in two important respects: a person may not have the opportunity to pass the information to every tie, and may be unwilling to pass the information to certain ties even when presented with the opportunity. Accounting for these two features reveals that, while additional random ties allow information to jump to distant regions of a network, they also change the composition of network neighborhoods. When the latter increases the proportion of neighbors to whom people are less willing to pass information, the result can be a net decrease in diffusion. I show that this is the case in heterogeneous, homophilous networks: the addition of random ties strictly impedes information dissemination, and the impediment is increasing in both original homophily and the number of new ties.",
    author = "Larson, {Jennifer M.}",
    year = "2017",
    doi = "10.1007/978-3-319-50901-3_36",
    language = "English (US)",
    volume = "693",
    pages = "449--458",
    journal = "Studies in Computational Intelligence",
    issn = "1860-949X",
    publisher = "Springer Verlag",

    }

    TY - JOUR

    T1 - Information diffusion in heterogeneous groups

    AU - Larson, Jennifer M.

    PY - 2017

    Y1 - 2017

    N2 - Standard approaches to the study of information diffusion draw on analogies to the transmission of diseases or computer viruses, and find that adding more random ties to a network increases the speed of information propagation through it. However, a person sharing information in a social network differs from a computer transmitting a virus in two important respects: a person may not have the opportunity to pass the information to every tie, and may be unwilling to pass the information to certain ties even when presented with the opportunity. Accounting for these two features reveals that, while additional random ties allow information to jump to distant regions of a network, they also change the composition of network neighborhoods. When the latter increases the proportion of neighbors to whom people are less willing to pass information, the result can be a net decrease in diffusion. I show that this is the case in heterogeneous, homophilous networks: the addition of random ties strictly impedes information dissemination, and the impediment is increasing in both original homophily and the number of new ties.

    AB - Standard approaches to the study of information diffusion draw on analogies to the transmission of diseases or computer viruses, and find that adding more random ties to a network increases the speed of information propagation through it. However, a person sharing information in a social network differs from a computer transmitting a virus in two important respects: a person may not have the opportunity to pass the information to every tie, and may be unwilling to pass the information to certain ties even when presented with the opportunity. Accounting for these two features reveals that, while additional random ties allow information to jump to distant regions of a network, they also change the composition of network neighborhoods. When the latter increases the proportion of neighbors to whom people are less willing to pass information, the result can be a net decrease in diffusion. I show that this is the case in heterogeneous, homophilous networks: the addition of random ties strictly impedes information dissemination, and the impediment is increasing in both original homophily and the number of new ties.

    UR - http://www.scopus.com/inward/record.url?scp=85007286314&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85007286314&partnerID=8YFLogxK

    U2 - 10.1007/978-3-319-50901-3_36

    DO - 10.1007/978-3-319-50901-3_36

    M3 - Article

    VL - 693

    SP - 449

    EP - 458

    JO - Studies in Computational Intelligence

    JF - Studies in Computational Intelligence

    SN - 1860-949X

    ER -