Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
36 lines
1.1 KiB
36 lines
1.1 KiB
import networkx as nx
|
|
|
|
|
|
def s_metric(G, normalized=True):
|
|
"""Returns the s-metric of graph.
|
|
|
|
The s-metric is defined as the sum of the products deg(u)*deg(v)
|
|
for every edge (u,v) in G. If norm is provided construct the
|
|
s-max graph and compute it's s_metric, and return the normalized
|
|
s value
|
|
|
|
Parameters
|
|
----------
|
|
G : graph
|
|
The graph used to compute the s-metric.
|
|
normalized : bool (optional)
|
|
Normalize the value.
|
|
|
|
Returns
|
|
-------
|
|
s : float
|
|
The s-metric of the graph.
|
|
|
|
References
|
|
----------
|
|
.. [1] Lun Li, David Alderson, John C. Doyle, and Walter Willinger,
|
|
Towards a Theory of Scale-Free Graphs:
|
|
Definition, Properties, and Implications (Extended Version), 2005.
|
|
https://arxiv.org/abs/cond-mat/0501169
|
|
"""
|
|
if normalized:
|
|
raise nx.NetworkXError("Normalization not implemented")
|
|
# Gmax = li_smax_graph(list(G.degree().values()))
|
|
# return s_metric(G,normalized=False)/s_metric(Gmax,normalized=False)
|
|
# else:
|
|
return float(sum([G.degree(u) * G.degree(v) for (u, v) in G.edges()]))
|
|
|