ash_model.measures.hyper_conformity¶

ash_model.measures.hyper_conformity(h, alphas, labels, s=1, profile_size=1, hierarchies=None, tid=None)[source]¶

Compute the Attribute-Profile Conformity for the considered graph

Parameters:

h (ASH) –
alphas (list) – list of damping factors
labels (list) – list of node categorical labels
s (int) –
profile_size (int) –
hierarchies (dict | None) – label hierarchies
tid (int | None) –

Returns:

conformity value for each node in [-1, 1]

Return type:

dict

Examples

Build the dataset described in the docs and compute hyper-conformity for label ‘color’ at tid=0:

>>> 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)
>>> res = hyper_conformity(h, alphas=[1], labels=['color'], s=1, profile_size=1, tid=0)
>>> len(res)
1
>>> ap = list(res[0].keys())[0]
>>> feat = list(res[0][ap].keys())[0]
>>> sorted(list(res[0][ap][feat].items()))[:3]
[('e1', 0.6287411100578744), ('e10', 0.5843765336901121), ('e100', 0.6358755716986335)]