Creating a Network¶
Weighted and Binary Graphs¶
Minimum Spanning Tree¶
why a connected graph?
Thresholding¶
The BrainNetwork Class¶
This is a very lightweight subclass of the networkx.Graph class. This means that any methods you can use on a networkx.Graph object can also be used on a BrainNetwork object, although the reverse is not true. We have added various methods that allow us to keep track of measures that have already been calculated. This is particularly useful later on when one is dealing with 1000 random graphs (or more!) and saves a lot of time.
All scona.BrainNetwork methods have a corresponding scona function. So while the scona.BrainNetwork methods can only be applied to scona.BrainNetwork objects, you can find the equivalent function in scona which can be used on a regular networkx.Graph object.
For example, if G is a scona.BrainNetwork object, you can threshold it by typing G.threshold(10). If nxG is a Networkx.Graph you can use scona.threshold_graph(nxG, 10) to perform the same function.
A BrainNetwork can be initialised from a networkx.Graph or from a correlation matrix represented as a pandas.DataFrame or numpy.array.