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Fusing data depth with complex networks: Community detection with prior information

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  • Tian, Yahui
  • Gel, Yulia R.

Abstract

A new nonparametric supervised algorithm is proposed for detecting multiple communities in complex networks using the Depth vs. Depth (DD(G)) classifier. The key idea behind the new clustering method is the notion of robust and data-driven data depth methodology that still remains new and unexplored in network sciences. The developed new DD(G)-method is inherently geometric and allows to simultaneously account for network communities and outliers. Although the data-based classifier operates within a supervised learning framework, the related nonparametric notion of depth in networks can be used in a more general context, including (semi) supervised and unsupervised learning. Utility of the new approach is illustrated by using the benchmark political blogs data, “dark” terrorist networks, and analysis of bill cosponsorship in the Italian Parliament.

Suggested Citation

  • Tian, Yahui & Gel, Yulia R., 2019. "Fusing data depth with complex networks: Community detection with prior information," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 99-116.
    • Handle: RePEc:eee:csdana:v:139:y:2019:i:c:p:99-116
    DOI: 10.1016/j.csda.2019.01.007
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    References listed on IDEAS

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    Cited by:

    1. Marcell Tamás Kurbucz & Attila Imre Katona & Zoltán Lantos & Zsolt Tibor Kosztyán, 2021. "The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    2. Zhang, Xu & Tian, Yahui & Guan, Guoyu & Gel, Yulia R., 2021. "Depth-based classification for relational data with multiple attributes," Journal of Multivariate Analysis, Elsevier, vol. 184(C).

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