Convex Relaxation Methods for Community Detection

• Li, Xiaodong ^[1] ; Chen, Yudong ^[2] ; Xu, Jiaming ^[3]
  1. [1] University of California System

     University of California System

     Estados Unidos

     
  2. [2] Cornell University

     Cornell University

     City of Ithaca, Estados Unidos

     
  3. [3] Duke University

     Duke University

     Township of Durham, Estados Unidos

     

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• Localización: Statistical science, ISSN 0883-4237, Vol. 36, Nº. 1, 2021, págs. 2-15
• Idioma: inglés
• DOI: 10.1214/19-STS715
• Texto completo no disponible (Saber más ...)
• Resumen

  • This paper surveys recent theoretical advances in convex optimization approaches for community detection. We introduce some important theoretical techniques and results for establishing the consistency of convex community detection under various statistical models. In particular, we discuss the basic techniques based on the primal and dual analysis. We also present results that demonstrate several distinctive advantages of convex community detection, including robustness against outlier nodes, consistency under weak assortativity, and adaptivity to heterogeneous degrees.

    This survey is not intended to be a complete overview of the vast literature on this fast-growing topic. Instead, we aim to provide a big picture of the remarkable recent development in this area and to make the survey accessible to a broad audience. We hope that this expository article can serve as an introductory guide for readers who are interested in using, designing, and analyzing convex relaxation methods in network analysis.