# Statistical validation of high-dimensional models of growing networks

@article{Medo2014StatisticalVO, title={Statistical validation of high-dimensional models of growing networks}, author={Mat{\'u}{\vs} Medo}, journal={Physical review. E, Statistical, nonlinear, and soft matter physics}, year={2014}, volume={89 3}, pages={ 032801 } }

The abundance of models of complex networks and the current insufficient validation standards make it difficult to judge which models are strongly supported by data and which are not. We focus here on likelihood maximization methods for models of growing networks with many parameters and compare their performance on artificial and real datasets. While high dimensionality of the parameter space harms the performance of direct likelihood maximization on artificial data, this can be improved by…

## 20 Citations

### Ranking nodes in growing networks: When PageRank fails

- Computer ScienceScientific reports
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It is shown that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters, and that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

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A general method for statistical parameter estimation and model selection that is applicable to growing multilayer networks and takes both the parameter errors and the model complexity into account and is computationally efficient and scalable to large networks.

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- Computer SciencePhysical review. E
- 2018

The fitness model yields the long-sought explanation for the initial attractivity K_{0}, an elusive parameter which was left unexplained within the framework of the preferential attachment model, and it is shown that theInitial attractivity is determined by the width of the fitness distribution.

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- Computer ScienceWebSci
- 2018

A new preferential attachment-based network growth model is proposed in order to explain two properties of growing networks: (1) the power-law growth of node degrees and (2) the decay of node relevance, and it is found that apart from being empirically observed in many systems, the exponential growth of the network size over time is the key to sustain thePower-laws growth of nodes degrees when node relevance decays.

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A Bayesian formulation of network archaeology is introduced, with a generalization of preferential attachment as the generative mechanism, and a sequential importance sampling algorithm to evaluate the posterior averages of this model, as well as an efficient heuristic that uncovers the history of a network in linear time.

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- Computer Science
- 2020

A novel analytical framework is proposed based on the time-invariance of the studied systems and it is shown that it is self-consistent only for two special network growth forms: the uniform and exponential network growth.

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