Eric Yanchenko
Biographic Data
| ID | 4163950 |
|---|---|
| NAME | Eric Yanchenko |
| GIVEN NAMES | Eric |
| FAMILY NAME | Yanchenko |
| SIGNATURE | YANCHENKO E |
| AFFILIATIONS | Akita International University |
| ORCID | 0000-0001-6075-6449 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Hypothesis testing for community structure in temporal networks using e-values
Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community structure in temporally evolving networks is more scarce. In particular, there are currently no statistical methods available to test for the presence of community structure in a sequence of networks evolving over time. In this work, we propose a simple yet powerful test …
A label-switching algorithm for fast core-periphery identification
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 Everett (Borgatti, S. P., & Everett M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an “ideal” CP structure. While…
A generalized hypothesis test for community structure in networks
Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in term…
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A generalized hypothesis test for community structure in networks
Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in term…
Hypothesis testing for community structure in temporal networks using e-values
Community structure in networks naturally arises in various applications. But while the topic has received significant attention for static networks, the literature on community structure in temporally evolving networks is more scarce. In particular, there are currently no statistical methods available to test for the presence of community structure in a sequence of networks evolving over time. In this work, we propose a simple yet powerful test …
A label-switching algorithm for fast core-periphery identification
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 Everett (Borgatti, S. P., & Everett M. G. (2000). Models of core/periphery structures. Social Networks, 21(4), 375–395.) proposed one of the most popular methods to identify and quantify CP structure by comparing the observed network with an “ideal” CP structure. While…
Complex Network Analysis Techniques (3 works) · Community structure (2 works) · Mental Health Research Topics (2 works) · Network structure (2 works) · Statistical hypothesis testing (2 works) · Advanced Graph Neural Networks (1 work) · Artificial Intelligence (1 work) · Baseline (sea (1 work) · Computer Science (1 work) · Econometrics (1 work)