cdlib.lifecycles.flow_null

cdlib.lifecycles.flow_null(lc: object, target: str, direction: str, min_branch_size: int = 1, iterations: int = 1000) → dict

Compare the flow with a null model. Each branch of each flow is compared with a null branch of the same size. The null model is generated by randomly sampling elements from the reference partition iterations times. The mean and standard deviation of the null model are used to compute a z-score for each branch, which is then used to compute a p-value.

Parameters:

  • lc – a CommunityMatching object

  • target – target set identifier

  • direction – temporal direction

  • min_branch_size – minimum size of a branch to be considered

  • iterations – number of random draws to be used to generate the null model

Returns:

a dictionary keyed by set identifier and valued by mean, std, and p-value

Example:

>>> from cdlib import TemporalClustering, LifeCycle
>>> from cdlib import algorithms
>>> from cdlib.lifecycles.algorithms import flow_null
>>> from networkx.generators.community import LFR_benchmark_graph
>>> tc = TemporalClustering()
>>> for t in range(0, 10):
>>>     g = LFR_benchmark_graph(
>>>         n=250,
>>>         tau1=3,
>>>         tau2=1.5,
>>>         mu=0.1,
>>>         average_degree=5,
>>>         min_community=20,
>>>         seed=10,
>>>     )
>>>     coms = algorithms.louvain(g)  # here any CDlib algorithm can be applied
>>>     tc.add_clustering(coms, t)
>>> events = LifeCycle(tc)
>>> events.compute_events("facets")
>>> validated = flow_null(events, "0_2", "+")