| Autor: |
Naomi A. Arnold, Raúl J. Mondragón, Richard G. Clegg |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Applied Network Science, Vol 8, Iss 1, Pp 1-18 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2364-8228 |
| DOI: |
10.1007/s41109-023-00574-3 |
| Popis: |
Abstract Often, due to prohibitively large size or to limits to data collecting APIs, it is not possible to work with a complete network dataset and sampling is required. A type of sampling which is consistent with Twitter API restrictions is uniform edge sampling. In this paper, we propose a methodology for the recovery of two fundamental network properties from an edge-sampled network: the degree distribution and the triangle count (we estimate the totals for the network and the counts associated with each edge). We use a Bayesian approach and show a range of methods for constructing a prior which does not require assumptions about the original network. Our approach is tested on two synthetic and three real datasets with diverse sizes, degree distributions, degree-degree correlations and triangle count distributions. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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