ash_model.measures.inclusiveness¶

ash_model.measures.inclusiveness(h, start=None, end=None)[source]¶

Computes the inclusiveness of the hypergraph over [start, end], defined as:

(# of non-facet hyperedges) / (total # of hyperedges)

A facet hyperedge is one that is not a subset of any other hyperedge. Non-facet hyperedges are those that are contained in at least one strictly larger hyperedge.

Parameters:

h (ASH) – an ASH instance
start (int | None) – optional start time (inclusive)
end (int | None) – optional end time (inclusive)

Returns:

a float in [0,1], or 0.0 if there are no hyperedges

Return type:

float

Examples

>>> import numpy as np, networkx as nx
>>> from ash_model.utils.networkx import from_networkx_maximal_cliques_list
>>> Gs = [nx.barabasi_albert_graph(100, 3, seed=i) for i in range(10)]
>>> rng = np.random.default_rng(42)
>>> for G in Gs:
...     for n in G.nodes():
...         G.nodes[n]['color'] = 'red' if rng.integers(0, 2) == 0 else 'blue'
>>> h = from_networkx_maximal_cliques_list(Gs)
>>> inclusiveness(h, start=0, end=0)
0.0