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SAINT: Supervised Actor Identification for Network Tuning

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Mining Social Networks and Security Informatics

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

Whenever the actors of a social network are not uniquely identifiable in the data, then entity resolution in the form of actor identification becomes a critical facet of a social network construction process. Here we develop SAINT, a pipeline for supervised entity resolution that uses relational information to improve, or tune, the quality of the constructed network. The first phase of SAINT uses attribute only based entity resolution to create an initial social network. Relational information between actors, actor network properties and other relational output of the first classification phase, are used in a second phase to improve the results of the original entity resolution. When compared to single phased approaches, the results from this two phased approach are consistently superior in both recall and precision measures. Embedded within SAINT are a series of evaluation checkpoints designed to measure both the quality of the individual classifiers and their impact within the entire pipeline. Our evaluation results provide insight on the potential propagation of error and open research questions for further improvement of the individual classifiers within the entire pipeline. As the main application of the process is to improve actor identification in social networks, we characterise the impact that entity resolution has on the final constructed network. We compare the network constructed using SAINT with a ground truth network using perfect entity resolution and use global and local network measures to study the differences.

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Author information

Authors and Affiliations

  1. University College Dublin, Dublin, Ireland

    Michael Farrugia & Neil Hurley

  2. University of St Andrews, St Andrews, Scotland, UK

    Aaron Quigley

Authors
  1. Michael Farrugia
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  2. Neil Hurley
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  3. Aaron Quigley
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Corresponding author

Correspondence to Michael Farrugia .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, TOBB University, Sogutozu Cad No. 43, Sogutozu Ankara, Turkey

    Tansel Özyer

  2. Information Technologies Institute, TUBITAK BILGEM, Gebze, Kocaeli, 41470, Turkey

    Zeki Erdem

  3. Computer Science, University of Calgary, University Dr. NW 2500, Calgary, T2N 1N4, Canada

    Jon Rokne

  4. American University of Sharjah, Universities City, Sharjah, Saudi Arabia

    Suheil Khoury

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Farrugia, M., Hurley, N., Quigley, A. (2013). SAINT: Supervised Actor Identification for Network Tuning. In: Özyer, T., Erdem, Z., Rokne, J., Khoury, S. (eds) Mining Social Networks and Security Informatics. Lecture Notes in Social Networks. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6359-3_6

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  • DOI: https://doi.org/10.1007/978-94-007-6359-3_6

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-6358-6

  • Online ISBN: 978-94-007-6359-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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