9 citations · 12 across the 4 of their papers we have counts for
4 papers
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
Benjamin Sischka, Göran Kauermann
This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that…
Stochastic Block Smooth Graphon Model
Benjamin Sischka, Göran Kauermann
The paper proposes the combination of stochastic blockmodels with smooth graphon models. The first allow for partitioning the set of individuals in a network into blocks which repr…
Mixture Models and Networks -- Overview of Stochastic Blockmodelling
Giacomo De Nicola, Benjamin Sischka, Göran Kauermann
Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of no…
EM-Based Smooth Graphon Estimation Using Bayesian and Spline-Based Approaches
Benjamin Sischka, Göran Kauermann
This paper proposes the estimation of a smooth graphon model for network data analysis using principles of the EM algorithm. The approach considers both variability with respect to…