10 citations · 13 across the 10 of their papers we have counts for
10 papers
Core-periphery identification in massive networks
Eric Yanchenko
Modern networks can be huge with millions or even billions of nodes and edges. Thus, algorithms must be capable of scaling to such large networks in order to be practically useful.…
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…
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…
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…
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…
Quantifying the presence/absence of meso-scale structures in networks
Eric Yanchenko
Meso-scale structures are network features where nodes with similar properties are grouped together instead of being treated individually. In this work, we provide formal and mathe…
Core-periphery structure in networks: a statistical exposition
Eric Yanchenko, Srijan Sengupta
Many real-world networks are theorized to have core-periphery structure consisting of a densely-connected core and a loosely-connected periphery. While this phenomenon has been ext…
A generalized hypothesis test for community structure in networks
Eric Yanchenko, Srijan Sengupta
Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exi…