netneurotools.metrics.search_information

netneurotools.metrics.search_information(W, D, has_memory=False)

Calculate search information.

This function implements search information, computes the amount of information (measured in bits) that a random walker needs to follow the shortest path between a given pair of nodes.

This function is adapted and optimized from the Brain Connectivity Toolbox.

Warning

Test before use.

Parameters:

• W ((N, N) ndarray) – Weighted/unweighted, directed/undirected connection weight matrix.

• D ((N, N) ndarray) – Weighted/unweighted, directed/undirected connection length or distance matrix. Please do the weight-to-distance beforehand.

• has_memory (bool, optional) – Memory for random walker, Default: False

Returns:

SI – Pairwise search information matrix. The diagonal is set to NaN. Edges without a valid shortest path are set to np.inf. It is not guaranteed to be symmetric even if input is symmetric.

Return type:

(N, N) ndarray

References

[1]

Rosvall, M., Trusina, A., Minnhagen, P., & Sneppen, K. (2005). Networks and cities: An information perspective. Physical Review Letters, 94(2), 028701.

[2]

Goñi, J., Van Den Heuvel, M. P., Avena-Koenigsberger, A., Velez de Mendizabal, N., Betzel, R. F., Griffa, A., … & Sporns, O. (2014). Resting-brain functional connectivity predicted by analytic measures of network communication. Proceedings of the National Academy of Sciences, 111(2), 833-838.