6 papers
Universal Inference for model selection on networks
Eric Yanchenko, Jonathan P. Williams, Ryan Martin
Model selection and hypothesis testing are important tasks on networks. A key challenge lies in the inherent dependence in network data, as well as the fact that typically only a s…
Hypothesis testing for community structure in temporal networks using e-values
Eric Yanchenko, Jonathan P. Williams, Ryan Martin
Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community st…
A label-switching algorithm for fast core-periphery identification
Eric Yanchenko, Srijan Sengupta
Core-periphery (CP) structure is frequently observed in networks where the nodes form two distinct groups: a small, densely interconnected core and a sparse periphery. Borgatti and…
Oral exams in introductory statistics class with non-native English speakers
Eric Yanchenko
Oral exams are a powerful tool to assess student's learning. This is particularly important in introductory statistics classes where students struggle to grasp various topics like…
Statistical inference for core-periphery structures
Eric Yanchenko, Srijan Sengupta, Diganta Mukherjee
Core-periphery (CP) structure is an important meso-scale network property where nodes group into a small, densely interconnected {core} and a sparse {periphery} whose members prima…
Graph sub-sampling for divide-and-conquer algorithms in large networks
Eric Yanchenko
As networks continue to increase in size, current methods must be capable of handling large numbers of nodes and edges in order to be practically relevant. Instead of working direc…