tethne.analyze.collection module

Methods for analyzing GraphCollections.

algorithm	Apply a method from NetworkX to all networkx.Graph objects in the GraphCollection G.
attachment_probability	Calculates the observed attachment probability for each node at each time-step.
connected	Performs analysis methods from networkx.connected on each graph in the collection.
delta	Updates a GraphCollection with deltas of a node attribute.
node_global_closeness_centrality	Calculates global closeness centrality for node in each graph in GraphCollection G.

tethne.analyze.collection.algorithm(G, method, **kwargs)

Apply a method from NetworkX to all networkx.Graph objects in the GraphCollection G.

For options, see the list of algorithms in the NetworkX documentation. Not all of these have been tested.

Parameters:

G : GraphCollection

The GraphCollection to analyze. The specified method will be applied to each graph in G.

method : string

Name of a method in NetworkX to execute on graph collection.

**kwargs

A list of keyword arguments that should correspond to the parameters of the specified method.

Returns:

results : dict

Indexed by element (node or edge) and graph index (e.g. date).

Raises:

ValueError

If no such method exists.

Examples

Betweenness centrality: (G is a GraphCollection)

>>> from tethne.analyze import collection
>>> BC = collection.algorithm(G, 'betweenness_centrality')
>>> print BC[0]
{1999: 0.010101651117889644,
2000: 0.0008689093723107329,
2001: 0.010504898852426189,
2002: 0.009338654511194512,
2003: 0.007519105636349891}

tethne.analyze.collection.attachment_probability(G)

Calculates the observed attachment probability for each node at each time-step.

Attachment probability is calculated based on the observed new edges in the next time-step. So if a node acquires new edges at time t, this will accrue to the node’s attachment probability at time t-1. Thus at a given time, one can ask whether degree and attachment probability are related.

Parameters:

G : GraphCollection

Must be sliced by ‘date’. See GraphCollection.slice().

Returns:

probs : dict

Keyed by index in G.graphs, and then by node.

tethne.analyze.collection.connected(G, method, **kwargs)

Performs analysis methods from networkx.connected on each graph in the collection.

Parameters:

G : GraphCollection

The GraphCollection to analyze. The specified method will be applied to each graph in G.

method : string

Name of method in networkx.connected.

**kwargs : kwargs

Keyword arguments, passed directly to method.

Returns:

results : dict

Keys are graph indices, values are output of method for that graph.

Raises:

ValueError

If name is not in networkx.connected, or if no such method exists.

tethne.analyze.collection.delta(G, attribute)

Updates a GraphCollection with deltas of a node attribute.

Parameters:

G : GraphCollection

attribute : str

Name of a node attribute in G.

Returns:

deltas : dict

tethne.analyze.collection.node_global_closeness_centrality(G, node)

Calculates global closeness centrality for node in each graph in GraphCollection G.