SarkarAndrisChapmanEtAl2019

Référence

Sarkar, D., Andris, C., Chapman, C.A., Sengupta, R. (2019) Metrics for characterizing network structure and node importance in Spatial Social Networks. International Journal of Geographical Information Science, 33(5):1017-1039. (Scopus )

Résumé

Social Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

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@ARTICLE { SarkarAndrisChapmanEtAl2019,
    AUTHOR = { Sarkar, D. and Andris, C. and Chapman, C.A. and Sengupta, R. },
    TITLE = { Metrics for characterizing network structure and node importance in Spatial Social Networks },
    JOURNAL = { International Journal of Geographical Information Science },
    YEAR = { 2019 },
    VOLUME = { 33 },
    NUMBER = { 5 },
    PAGES = { 1017-1039 },
    NOTE = { cited By 0 },
    ABSTRACT = { Social Network Analysis offers powerful tools to analyze the structure of relationships between a set of people. However, the addition of spatial information poses new challenges, as nodes are embedded simultaneously in network space and Euclidean space. While nearby nodes may not form social ties, ties may exist at a distance, a configuration ill-suited for traditional spatial metrics that assume adjacent objects are related. As such, there are relatively few metrics to describe these nuanced situations. We advance the burgeoning field of spatial social network analysis by introducing a set of new metrics. Specifically, we introduce the spatial social network schema, tuning parameter and the flattening ratio, each of which leverages the notion of ‘distance’ to augment insights obtained by relying on topology alone. These methods are used to answer the questions: What is the social and spatial structure of the network? Who are the key individuals at different spatial scales? We use two synthetic networks with properties mimicking the ones reported in the literature as validation datasets and a case study of employer–employee network. The methods characterize the employer–employee as spatially loose with predominantly local connections and identify key individuals responsible for keeping the network connected at different spatial scales. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. },
    AFFILIATION = { Department of Geography, National University of Singapore, Singapore; Department of Geography, McGill University, Montreal, Canada; Department of Geography, Pennsylvania State University, University Park, PA, United States; School of Environment, McGill University, Montreal, Canada; Department of Anthropology, McGill University, Montreal, Canada; Shaanxi Key Laboratory for Animal Conservation, Northwest University, Xi’an, China; School of Life Sciences, University of KwaZulu-Natal, Pietermaritzburg, South Africa },
    AUTHOR_KEYWORDS = { metrics; network structure; node importance; social relationships; Spatial Social Network },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1080/13658816.2019.1567736 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062520783&doi=10.1080%2f13658816.2019.1567736&partnerID=40&md5=9dc01a32c0f4cdbfb12d5fa254e01809 },
}

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