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A topological framework to explore longitudinal social networks

Author

Listed:
  • Shahadat Uddin

    (The University of Sydney)

  • Arif Khan

    (The University of Sydney)

  • Liaquat Hossain

    (The University of Hong Kong
    The University of Sydney)

  • Mahendra Piraveenan

    (The University of Sydney)

  • Sven Carlsson

    (Lund University)

Abstract

Longitudinal networks evolve over time through the creation and/or deletion of links among a set of actors (e.g., individuals or organizations). A longitudinal network can be viewed as a single static network (i.e., structure of network is fixed) that aggregates all the edges observed over some time period or as a series of static networks observed in different point of time over the entire network observation period (i.e., structure of network is changing over time). The understanding of the underlying structural changes of longitudinal networks and contributions of individual actors to these changes enable researchers to investigate different structural properties of such networks. By following a topological approach (i.e., static topology and dynamic topology), this paper first proposes a framework to analyze longitudinal social networks. In static topology, social networks analysis (SNA) methods are applied to the aggregated network of entire observation period. Smaller segments of network data (i.e., short-interval network) that are accumulated in less time compared to the entire network observation period are used in the dynamic topology for analysis purposes. Based on this framework, this study then conducts topological analysis of two longitudinal networks to explore over time actor-level dynamics during different phases of these two networks. The proposed topological framework can be utilized to explore structural vulnerabilities and evolutionary trend of various longitudinal social networks (e.g., disease spread network and computer virus network). This will eventually lead to better authorization and control over such networks. For network science researchers, this framework will bring new research opportunities to enhance our present knowledge about different aspects (e.g., network disintegration and contribution of individual actor’s to network evolution) of longitudinal social networks.

Suggested Citation

  • Shahadat Uddin & Arif Khan & Liaquat Hossain & Mahendra Piraveenan & Sven Carlsson, 2015. "A topological framework to explore longitudinal social networks," Computational and Mathematical Organization Theory, Springer, vol. 21(1), pages 48-68, March.
    • Handle: RePEc:spr:comaot:v:21:y:2015:i:1:d:10.1007_s10588-014-9176-3
    DOI: 10.1007/s10588-014-9176-3
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    References listed on IDEAS

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    1. Garry Robins & Philippa Pattison & Stanley Wasserman, 1999. "Logit models and logistic regressions for social networks: III. Valued relations," Psychometrika, Springer;The Psychometric Society, vol. 64(3), pages 371-394, September.
    2. Gerhard G. Van De Bunt & Marijtje A.J. Van Duijn & Tom A.B. Snijders, 1999. "Friendship Networks Through Time: An Actor-Oriented Dynamic Statistical Network Model," Computational and Mathematical Organization Theory, Springer, vol. 5(2), pages 167-192, July.
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    Cited by:

    1. Raúl M. Ortiz-Gaona & Marcos Postigo-Boix & José L. Melús-Moreno, 2021. "Extent prediction of the information and influence propagation in online social networks," Computational and Mathematical Organization Theory, Springer, vol. 27(2), pages 195-230, June.
    2. Shakir Karim & Shahadat Uddin & Tasadduq Imam & Mohammad Ali Moni, 2020. "A Systematic Review of Network Studies Based on Administrative Health Data," IJERPH, MDPI, vol. 17(7), pages 1-20, April.

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