network

matrix_index(user)	Returns the keys associated with each axis of the matrices.
matrix_directed_weighted(user[, interaction])	Returns a directed, weighted matrix for call, text and call duration.
matrix_directed_unweighted(user)	Returns a directed, unweighted matrix where an edge exists if there is at least one call or text.
matrix_undirected_weighted(user[, interaction])	Returns an undirected, weighted matrix for call, text and call duration where an edge exists if the relationship is reciprocated.
matrix_undirected_unweighted(user)	Returns an undirected, unweighted matrix where an edge exists if the relationship is reciprocated.
clustering_coefficient_unweighted(user)	The clustering coefficient of the user in the unweighted, undirected ego network.
clustering_coefficient_weighted(user[, …])	The clustering coefficient of the user’s weighted, undirected network.
assortativity_indicators(user)	Computes the assortativity of indicators.
assortativity_attributes(user)	Computes the assortativity of the nominal attributes.

Matrices

Networks are generated using User.network, with neighbors sorted by name (see matrix_index() for more details).

We load the following examples using the network user from the bandicoot source directory using:

>>> ego = bc.read_csv('ego', 'bandicoot/tests/samples/network', network=True)

directed, weighted

matrix_directed_weighted() returns a directed, weighted matrix for call, text and call duration. By default, interaction is None: the weight is the number of 30 minutes periods with at least one call or one text. Summing call and texts is not accurate, counting periods of activity leads to a better understanding of the interactions.

>>> bc.network.matrix_index(ego)
['ego', 'A', 'B', 'D', 'F', 'H']

>>> m = bc.network.matrix_directed_weighted(ego)
>>> m

[[0, 3, 2, 1, 2, 0],
 [1, 0, 1, 0, 0, 0],
 [2, 1, 0, 1, 0, 0],
 [0, 0, 0, 0, 0, 0],
 [1, 0, 0, 0, 0, 1],
 [0, 0, 0, 0, 1, 0]]

The cell m[0][1], equal to 3, is the number of interactions from ego (index 0) to A (index 1).

A None cell means that we have no information of the interactions between two users, who are both out of the network.

directed, unweighted

matrix_directed_unweighted() returns a directed, unweighted matrix where an edge exists if there is at least one call or text, in both direction.

>>> bc.network.matrix_directed_unweighted(ego)

[[0, 1, 1, 1, 1, 0],
 [1, 0, 1, 0, 0, 0],
 [1, 1, 0, 1, 0, 0],
 [0, 0, 0, 0, 0, 0],
 [1, 0, 0, 0, 0, 1],
 [0, 0, 0, 0, 1, 0]]

undirected, weighted

matrix_undirected_weighted() returns an undirected, weighted matrix for call, text and call duration. An edge only exists if the relationship is reciprocated. It counts the total number of interactions in both directions.

>>> bc.network.matrix_undirected_weighted(ego)

[[0, 4, 4, 0, 3, 0],
 [4, 0, 2, 0, 0, 0],
 [4, 2, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0],
 [3, 0, 0, 0, 0, 2],
 [0, 0, 0, 0, 2, 0]]

undirected, unweighted

matrix_undirected_unweighted() returns an undirected, unweighted matrix where an edge exists if the relationship is reciprocated.

>>> bc.network.matrix_undirected_unweighted(ego)

[[0, 1, 1, 0, 1, 0],
 [1, 0, 1, 0, 0, 0],
 [1, 1, 0, 0, 0, 0],
 [0, 0, 0, 0, 0, 0],
 [1, 0, 0, 0, 0, 1],
 [0, 0, 0, 0, 1, 0]]