Python Module networkmeasures¶
Defines network measures for quantum mutual information matrices.
Authors
David L. Vargas (original version)
Logan Hillberry
Jaschke (adaption for OSMPS module)
- networkmeasures.networkmeasures(resdict)[source]¶
Set the network measures to a result dictionary.
Arguments
- resdictdictionary
Contains the results of an MPS simulation. The mutual information matrix measurement is required. This measurement is stored as key
MIM. (Could extend function such that all single and two site reduced density matrices are sufficient as well.)
Details
The following flags will be set (or overwritten if existent):
nwm_clustering: Clustering coefficient (networkmeasures.clustering())nwm_density: Density of the mutual information matrix (networkmeasures.density())nwm_disparity: Disparity of the mutual information matrix (networkmeasures.disparity())nwm_pearson: Pearson coefficient and 2p taisl of the mutual information matrix (networkmeasures.disparity())
- networkmeasures.clustering(matrix)[source]¶
Calculates the clustering coefficient as it is defined in equation (7.39) of Mark Newman’s book on networks (page 199).
Arguments
- matrix2d numpy array
Contains the mutual information matrix.
- networkmeasures.density(matrix)[source]¶
Calculates density, also termed connectance in some literature. Defined on page 134 of Mark Newman’s book on networks.
Arguments
- matrix2d numpy array
Contains the mutual information matrix.
- networkmeasures.disparity(matrix)[source]¶
Disparity defined on page 199 of doi:10.1016/j.physrep.2005.10.009 Equation (2.39), Here I take the average of this quantity over the entire network
Arguments
- matrix2d numpy array
Contains the mutual information matrix.
- networkmeasures.pearson(matrix)[source]¶
Calculates the Pearsons correlation coefficient and the 2-tailed p-value. For definitions see scipy.stats.pearsonr. Function returns a tuple with matrix containing Pearson correlation coefficients and a second matrix with the 2-tailed p-values.
Arguments
- matrix2d numpy array
Contains the mutual information matrix.