Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2014 Jul 9;9(7):e100805. doi: 10.1371/journal.pone.0100805

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© 2014 Esposito et al

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

PMC Copyright notice

A Example of an adjacency matrix in a random network with pruning parameter and symmetry measure According with the p-value test with the null hypothesis of random connectivity and with a level of confidence of the symmetry of this network is significant if the distribution of the initial connections is uniform but is non-significant if the initial distribution of the connections is Gaussian. Therefore, in the first case it should be regarded as a non-random network whereas in the second case as a random network. B The same as A but with pruning parameter and symmetry measure In this case, with the same hypothesis test, the situation is reversed: the network should be considered random for initial uniform distribution of connections, but non-random for initial Gaussian-distributed connections (see the discussion in the text).

Inline graphic — A Example of an adjacency matrix in a random network with pruning parameter and symmetry measure According with the p-value test with the null hypothesis of random connectivity and with a level of confidence of the symmetry of this network is significant if the distribution of the initial connections is uniform but is non-significant if the initial distribution of the connections is Gaussian. Therefore, in the first case it should be regarded as a non-random network whereas in the second case as a random network. B The same as A but with pruning parameter and symmetry measure In this case, with the same hypothesis test, the situation is reversed: the network should be considered random for initial uniform distribution of connections, but non-random for initial Gaussian-distributed connections (see the discussion in the text).