Are extreme value estimation methods useful for network data?

Phyllis Wan, Tiandong Wang, Richard A. Davis, Sidney I. Resnick

Research output: Contribution to journalArticlepeer-review


Preferential attachment is an appealing edge generating mechanism for modeling social networks. It provides both an intuitive description of network growth and an explanation for the observed power laws in degree distributions. However, there are often limitations in fitting parametric network models to data due to the complex nature of real-world networks. In this paper, we consider a semi-parametric estimation approach by looking at only the nodes with large in- or out-degrees of the network. This method examines the tail behavior of both the marginal and joint degree distributions and is based on extreme value theory. We compare it with the existing parametric approaches and demonstrate how it can provide more robust estimates of parameters associated with the network when the data are corrupted or when the model is misspecified.

05C80, 90B15, 62F12

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Dec 19 2017
Externally publishedYes


  • Estimation
  • Multivariate heavy-tailed statistics
  • Power laws
  • Preferential attachment
  • Regular variation

ASJC Scopus subject areas

  • General

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