10 citations · 13 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
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…