Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data
A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled....
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MDPI AG
2018-04-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/20/4/257 |
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| author | Alexander P. Kartun-Giles Dmitri Krioukov James P. Gleeson Yamir Moreno Ginestra Bianconi |
| author_facet | Alexander P. Kartun-Giles Dmitri Krioukov James P. Gleeson Yamir Moreno Ginestra Bianconi |
| author_sort | Alexander P. Kartun-Giles |
| collection | DOAJ |
| description | A projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing) and equilibrium (static) sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree) with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling. |
| first_indexed | 2024-04-11T22:45:12Z |
| format | Article |
| id | doaj.art-5ed85888f4e541e19c8b85fcf5ce87f0 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T22:45:12Z |
| publishDate | 2018-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-5ed85888f4e541e19c8b85fcf5ce87f02022-12-22T03:58:48ZengMDPI AGEntropy1099-43002018-04-0120425710.3390/e20040257e20040257Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled DataAlexander P. Kartun-Giles0Dmitri Krioukov1James P. Gleeson2Yamir Moreno3Ginestra Bianconi4School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UKDepartments of Physics, Mathematics, and Electrical & Computer Engineering, Northeastern University, Boston 02120, MA, USAMACSI, Department of Mathematics and Statistics, University of Limerick, Limerick V94 T9PX, IrelandInstitute for Biocomputation and Physics of Complex Systems (BIFI), University of Zaragoza, Zaragoza 50013, SpainSchool of Mathematical Sciences, Queen Mary University of London, London E1 4NS, UKA projective network model is a model that enables predictions to be made based on a subsample of the network data, with the predictions remaining unchanged if a larger sample is taken into consideration. An exchangeable model is a model that does not depend on the order in which nodes are sampled. Despite a large variety of non-equilibrium (growing) and equilibrium (static) sparse complex network models that are widely used in network science, how to reconcile sparseness (constant average degree) with the desired statistical properties of projectivity and exchangeability is currently an outstanding scientific problem. Here we propose a network process with hidden variables which is projective and can generate sparse power-law networks. Despite the model not being exchangeable, it can be closely related to exchangeable uncorrelated networks as indicated by its information theory characterization and its network entropy. The use of the proposed network process as a null model is here tested on real data, indicating that the model offers a promising avenue for statistical network modelling.http://www.mdpi.com/1099-4300/20/4/257networks modelsprojectivity and exchangeabilitynetwork entropyinformation theory of networks |
| spellingShingle | Alexander P. Kartun-Giles Dmitri Krioukov James P. Gleeson Yamir Moreno Ginestra Bianconi Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data Entropy networks models projectivity and exchangeability network entropy information theory of networks |
| title | Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data |
| title_full | Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data |
| title_fullStr | Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data |
| title_full_unstemmed | Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data |
| title_short | Sparse Power-Law Network Model for Reliable Statistical Predictions Based on Sampled Data |
| title_sort | sparse power law network model for reliable statistical predictions based on sampled data |
| topic | networks models projectivity and exchangeability network entropy information theory of networks |
| url | http://www.mdpi.com/1099-4300/20/4/257 |
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