A generalized hypothesis test for community structure in networks
Bibliographic Data
| ID | 6161520 |
|---|---|
| Authors | Eric Yanchenko (0000-0001-6075-6449, North Carolina State University, corresponding author), Srijan Sengupta (0000-0001-6889-8599, North Carolina State University) |
| Year | 2024 |
| Volume | 12 |
| Issue | 2 |
| Pages | 122-138 |
| Publication date | 2024-03-11 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2024.1 |
| OpenAlex | W4392661664 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
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 terms of a general and model-agnostic community structure parameter. Leveraging this parameter, we propose a simple and interpretable test statistic used to formulate two separate hypothesis testing frameworks. The first is an asymptotic test against a baseline value of the parameter while the second tests against a baseline model using bootstrap-based thresholds. We prove theoretical properties of these tests and demonstrate how the proposed method yields rich insights into real-world datasets
Baseline (sea · Community structure · Econometrics · Framing (construction · Geography · Machine learning · Statistic · Statistical hypothesis testing · Statistics · Test (biology · Test statistic · Complex Network Analysis Techniques · Computer Science · Mathematics · Mental Health Research Topics · Opinion Dynamics and Social Influence · Artificial Intelligence
Finding community structure in networks using the eigenvectors of matrices
Maximizing the spread of influence through a social network
Community detection in graphs
Latent Space Approaches to Social Network Analysis
Finding community structure in very large networks
Stochastic blockmodels and community structure in networks
Collective dynamics of ‘small-world’ networks
Fast unfolding of communities in large networks
Emergence of Scaling in Random Networks
Models of core/periphery structures
Stochastic blockmodels
An introduction to exponential random graph (p*) models for social networks
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |