1 files changed, 37 insertions, 0 deletions
diff --git a/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/smetric.py b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/smetric.py
new file mode 100644
index 0000000..0e801bf
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+++ b/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/smetric.py
@@ -0,0 +1,37 @@
+import networkx as nx
+#from networkx.generators.smax import li_smax_graph
+
+def s_metric(G, normalized=True):
+    """Return 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.
+           http://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_iter()]))
+