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Scaling Up Integrated Structural and Content-Based Network Analysis

Author

Listed:
  • Jennifer Golbeck

    (University of Maryland)

  • Jeff Gerhard

    (University of Maryland)

  • Farrah O’Colman

    (University of Maryland)

  • Ryan O’Colman

    (University of Maryland)

Abstract

Structural analysis of social networks can provide important insights into the clusters and important nodes. However, it is silent on the content-based reasons for importance or commonality. This additional level of insight requires sampling content from nodes and processing it to distill new insights. That is done effectively by human analysts, but as networks grow into Big Data scale, human analysis is not possible. This raises the question of whether automated techniques can mimic the same results humans find. In this paper, we demonstrate how topic modeling can be applied, filtered, and adapted to produce easy-to-understand keywords that represent important clusters in a network. Those keywords reflect the insights achieved by human analysts doing a manual content-based analysis of the network features. While humans should never be removed from the analysis process, this work shows how automated techniques can be integrated to scale humans’ ability to gain insights in large networks.

Suggested Citation

  • Jennifer Golbeck & Jeff Gerhard & Farrah O’Colman & Ryan O’Colman, 2018. "Scaling Up Integrated Structural and Content-Based Network Analysis," Information Systems Frontiers, Springer, vol. 20(6), pages 1191-1202, December.
    • Handle: RePEc:spr:infosf:v:20:y:2018:i:6:d:10.1007_s10796-017-9783-x
    DOI: 10.1007/s10796-017-9783-x
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    References listed on IDEAS

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    1. Cai-Nicolas Ziegler & Georg Lausen, 2005. "Propagation Models for Trust and Distrust in Social Networks," Information Systems Frontiers, Springer, vol. 7(4), pages 337-358, December.
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    Cited by:

    1. Ludovico Boratto & Salvatore Carta & Andreas Kaltenbrunner & Matteo Manca, 2018. "Guest Editorial: Behavioral-Data Mining in Information Systems and the Big Data Era," Information Systems Frontiers, Springer, vol. 20(6), pages 1153-1156, December.

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