latapy_clustering¶
-
latapy_clustering
(G, nodes=None, mode='dot')[source]¶ Compute a bipartite clustering coefficient for nodes.
The bipartie clustering coefficient is a measure of local density of connections defined as [1]:
c_u = \frac{\sum_{v \in N(N(v))} c_{uv} }{|N(N(u))|}
where N(N(u)) are the second order neighbors of u in G excluding u, and c_{uv} is the pairwise clustering coefficient between nodes u and v.
The mode selects the function for c_{uv} which can be:
dot:
c_{uv}=\frac{|N(u)\cap N(v)|}{|N(u) \cup N(v)|}
min:
c_{uv}=\frac{|N(u)\cap N(v)|}{min(|N(u)|,|N(v)|)}
max:
c_{uv}=\frac{|N(u)\cap N(v)|}{max(|N(u)|,|N(v)|)}
Parameters: - G (graph) – A bipartite graph
- nodes (list or iterable (optional)) – Compute bipartite clustering for these nodes. The default is all nodes in G.
- mode (string) – The pariwise bipartite clustering method to be used in the computation. It must be “dot”, “max”, or “min”.
Returns: clustering – A dictionary keyed by node with the clustering coefficient value.
Return type: dictionary
Examples
>>> from networkx.algorithms import bipartite >>> G = nx.path_graph(4) # path graphs are bipartite >>> c = bipartite.clustering(G) >>> c[0] 0.5 >>> c = bipartite.clustering(G,mode='min') >>> c[0] 1.0
See also
robins_alexander_clustering()
,square_clustering()
,average_clustering()
References
[1] Latapy, Matthieu, Clémence Magnien, and Nathalie Del Vecchio (2008). Basic notions for the analysis of large two-mode networks. Social Networks 30(1), 31–48.