2.2.1. Network Statistics.¶
This python file contains functions for standard network statistics.
-
dynamical_networks.analysis.statistics.betweenness(A)[source]¶ This function takes the time varying adjacency matrix and returns the network betweenness in time.
- Parameters
A (array) – Time varying adjacency matrix.
- Kwargs:
plotting (bool): Plotting for user interpretation. defaut is False.
- Returns
Statistic array over time.
- Return type
(array)
-
dynamical_networks.analysis.statistics.capacity(A)[source]¶ This function takes the time varying adjacency matrix and returns the network capacity in time.
- Parameters
A (array) – Time varying adjacency matrix.
- Kwargs:
plotting (bool): Plotting for user interpretation. defaut is False.
- Returns
Statistic array over time.
- Return type
(array)
-
dynamical_networks.analysis.statistics.centrality(A)[source]¶ This function takes the time varying adjacency matrix and returns the network centrality in time.
- Parameters
A (array) – Time varying adjacency matrix.
- Kwargs:
plotting (bool): Plotting for user interpretation. defaut is False.
- Returns
Statistic array over time.
- Return type
(array)
-
dynamical_networks.analysis.statistics.node_degree_distribution(A)[source]¶ This function takes the time varying adjacency matrix and returns the node degree distribution in time.
- Parameters
A (array) – Time varying adjacency matrix.
- Kwargs:
plotting (bool): Plotting for user interpretation. defaut is False.
- Returns
Statistic array over time.
- Return type
(array)
The following is an example:
from dynamical_networks.analysis.statistics import centrality, capacity, node_degree_distribution, betweenness
from dynamical_networks.simulate.PG_network import PG_network
A = PG_network()
S = [centrality(A), capacity(A), node_degree_distribution(A), betweenness(A)]