.png){kind=icon}
.png){kind=icon}
Return
Network Estimation via Graphon With Node Features
.png){kind=icon}
OA
AI
DOI：10.1109/TNSE.2020.2973994.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Estimation
Social networking (online)
Tuning
Smoothing methods
Synthetic aperture sonar
Convergence
NIST
consistency
exchangeable graph model
feature assisted neighborhood smoothing (FANS)
generative model
nonparametric
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K
