brainconn.distance.search_information

search_information(adjacency, transform=None, has_memory=False)[source]

Calculates search information of adjacency.

Computes the amount of information (measured in bits) that a random walker needs to follow the shortest path between a given pair of nodes [1] [2].

Parameters:
  • adjacency ((N x N) array_like) – Weighted/unweighted, direct/undirected connection weight/length array
  • transform (str, optional) – If adjacency is a connection weight array, specify a transform to map input connection weights to connection lengths. Options include [‘log’, ‘inv’], where ‘log’ is -np.log(adjacency) and ‘inv’ is 1/adjacency. Default: None
  • has_memory (bool, optional) – This flag defines whether or not the random walker “remembers” its previous step, which has the effect of reducing the amount of information needed to find the next state. Default: False
Returns:

SI – Pair-wise search information array. Note that SI[i,j] may be different from SI[j,i]`; hence, SI is not a symmetric matrix even when adjacency is symmetric.
Return type:

(N x N) ndarray

References

[1]Goni, J., van den Heuvel, M. P., Avena-Koenigsberger, A., de Mendizabal, N. V., Betzel, R. F., Griffa, A., Hagmann, P., Corominas-Murtra, B., Thiran, J-P., & 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.
[2]Rosvall, M., Trusina, A., Minnhagen, P., & Sneppen, K. (2005). Networks and cities: An information perspective. Physical Review Letters, 94(2), 028701.