Abstract:
One popular model for network analysis is the exchangeable graph model (ExGM), which is characterized by a two-dimensional function known as a graphon. Estimating an unde...Show MoreMetadata
Abstract:
One popular model for network analysis is the exchangeable graph model (ExGM), which is characterized by a two-dimensional function known as a graphon. Estimating an underlying graphon becomes the key of such analysis. Several nonparametric estimation methods have been proposed, and some are provably consistent. However, if certain useful features of the nodes (e.g., age and schools in a social network context) are available, none of these methods were designed to incorporate this source of information to help with the estimation. This paper develops a consistent graphon estimation method that integrates information from both the adjacency matrix itself and node features. We show that properly leveraging the features can improve the estimation. A cross-validation method is proposed to automatically select the tuning parameter of the method.
Published in: IEEE Transactions on Network Science and Engineering ( Volume: 7, Issue: 3, 01 July-Sept. 2020)