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

Are Extreme Value Estimation Methods Useful for Network Data?