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An empirical comparison of influence measurements for social network analysis

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Abstract

The studying of social influence can be used to understand and solve many complicated problems in social network analysis such as predicting influential users. This paper focuses on the problem of predicting influential users on social networks. We introduce a three-level hierarchy that classifies the influence measurements. The hierarchy categorizes the influence measurements by three folds, i.e., models, types and algorithms. Using this hierarchy, we classify the existing influence measurements. We further compare them based on an empirical analysis in terms of performance, accuracy and correlation using datasets from two different social networks to investigate the feasibility of influence measurements. Our results show that predicting influential users does not only depend on the influence measurements but also on the nature of social networks. Our goal is to introduce a standardized baseline for the problem of predicting influential users on social networks.

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Notes

  1. http://www.Flicker.com.

  2. http://www.digg.com.

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Authors and Affiliations

  1. University of Bridgeport, 167 Park St, Bridgeport, CT, USA

    Khaled Almgren & Jeongkyu Lee

Authors
  1. Khaled Almgren
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  2. Jeongkyu Lee
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Correspondence to Khaled Almgren.

Appendix

Appendix

See Table 8.

Table 8 Classification of the state of art by their definitions, models, number of measurements, types, algorithms and datasets
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Almgren, K., Lee, J. An empirical comparison of influence measurements for social network analysis. Soc. Netw. Anal. Min. 6, 52 (2016). https://doi.org/10.1007/s13278-016-0360-y

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