Sampling methods and estimation of triangle count distributions in large networks
Bibliographic Data
| ID | 6161475 |
|---|---|
| Authors | Nelson Antunes (0000-0001-6071-1099, University of Algarve, corresponding author), Tianjian Guo (0000-0002-1111-3540, University of North Carolina at Chapel Hill), Vladas Pipiras (0000-0001-6838-6294, University of North Carolina at Chapel Hill) |
| Year | 2021 |
| Volume | 9 |
| Issue | S1 |
| Pages | S134-S156 |
| Publication date | 2021-02-26 |
| 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.2021.2 |
| OpenAlex | W3134137072 |
| Language | EN |
| Citations received | 2 |
| References cited | 20 |
This paper investigates the distributions of triangle counts per vertex and edge, as a means for network description, analysis, model building, and other tasks. The main interest is in estimating these distributions through sampling, especially for large networks. A novel sampling method tailored for the estimation analysis is proposed, with three sampling designs motivated by several network access scenarios. An estimation method based on inversion and an asymptotic method are developed to recover the entire distribution. A single method to estimate the distribution using multiple samples is also considered. Algorithms are presented to sample the network under the various access scenarios. Finally, the estimation methods on synthetic and real-world networks are evaluated in a data study
Algorithm · Computer vision · Data mining · Estimation · Sample (material · Sampling (signal processing · Statistics · Advanced Graph Neural Networks · Complex Network Analysis Techniques · Computer Science · Mathematics · Topological and Geometric Data Analysis
| Citations per year | 0.4 |
|---|---|
| Citation span | 2021 - 2024 (4) |
| Highly cited | No |