7 citations · 16 across the 8 of their papers we have counts for
8 papers
Hierarchical Clustering of Networks via Hierarchical Distance Matrices
Li Chen, Nathaniel Josephs, Eric D. Kolaczyk +1
Clustering populations of networks while recovering their latent hierarchical organization is a fundamental yet largely unexplored problem in network analysis. To formalize this, w…
Nested stochastic block model for simultaneously clustering networks and nodes
Nathaniel Josephs, Arash A. Amini, Marina Paez +1
We introduce the nested stochastic block model (NSBM) to cluster a collection of networks while simultaneously detecting communities within each network. NSBM has several appealing…
Communication network dynamics in a large organizational hierarchy
Nathaniel Josephs, Sida Peng, Forrest W. Crawford
Most businesses impose a supervisory hierarchy on employees to facilitate management, decision-making, and collaboration, yet routine inter-employee communication patterns within w…
Bayesian community detection for networks with covariates
Luyi Shen, Arash Amini, Nathaniel Josephs +1
The increasing prevalence of network data in a vast variety of fields and the need to extract useful information out of them have spurred fast developments in related models and al…
A sample size heuristic for network scale-up studies
Nathaniel Josephs, Dennis M. Feehan, Forrest W. Crawford
The network scale-up method (NSUM) is a survey-based method for estimating the number of individuals in a hidden or hard-to-reach subgroup of a general population. In NSUM surveys,…
Network Recovery from Unlabeled Noisy Samples
Nathaniel Josephs, Wenrui Li, Eric D. Kolaczyk
There is a growing literature on the statistical analysis of multiple networks in which the network is the fundamental data object. However, most of this work requires networks on…
A spectral-based framework for hypothesis testing in populations of networks
Li Chen, Nathaniel Josephs, Lizhen Lin +2
In this paper, we propose a new spectral-based approach to hypothesis testing for populations of networks. The primary goal is to develop a test to determine whether two given samp…
Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome
Nathaniel Josephs, Lizhen Lin, Steven Rosenberg +1
While the study of a single network is well-established, technological advances now allow for the collection of multiple networks with relative ease. Increasingly, anywhere from se…