Configuration model for correlation matrices preserving the node strength.

  title={Configuration model for correlation matrices preserving the node strength.},
  author={Naoki Masuda and Sadamori Kojaku and Yukie Sano},
  journal={Physical review. E},
  volume={98 1-1},
Correlation matrices are a major type of multivariate data. To examine properties of a given correlation matrix, a common practice is to compare the same quantity between the original correlation matrix and reference correlation matrices, such as those derived from random matrix theory, that partially preserve properties of the original matrix. We propose a model to generate such reference correlation and covariance matrices for the given matrix. Correlation matrices are often analyzed as… 

Constructing networks by filtering correlation matrices: a null model approach

A method to create networks from correlation matrices based on optimization with regularization, where an edge is laid between each pair of nodes if and only if the edge is unexpected from a null model is proposed.

Scale-resolved analysis of brain functional connectivity networks with spectral entropy

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A principled (and practical) test for network comparison

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Sector Neutral Portfolios: Long Memory Motifs Persistence in Market Structure Dynamics

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Simplicial persistence of financial markets: filtering, generative processes and portfolio risk

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Network analysis of the immune state of mice

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Spontaneous back-pain alters randomness in functional connections in large scale brain networks: A random matrix perspective



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The key idea is to use three-way partial correlation or partial mutual information to measure the strength of the association between the two neighboring nodes of a focal node relative to the amount of pseudo-correlation expected from indirect paths between the nodes.

Analytical maximum-likelihood method to detect patterns in real networks

This work proposes a fast method that allows one to obtain expectation values and standard deviations of any topological property analytically across the entire graph ensemble, for any binary, weighted, directed or undirected network.

On the use of correlation as a measure of network connectivity

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This work introduces, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques, and can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anti-correlated.

Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

It is argued that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

The entropy of randomized network ensembles

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Entropy of network ensembles.

  • G. Bianconi
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2009
The structural entropy is defined and evaluated, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence, and a solution to the paradox is proposed by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy.