On 08.02.2016 11:31, Danilo Giuffrida wrote:
> Yes. For example in the original directed network I have the triangle:
> (1)<--(2)<-->(3)-->(1)
> then I obtain the undirected network by means of g.set_directed(False)
> and evaluate the local clustering of node 1, finding C(1)=2 (instead
> of C(1)=1). This happens for all the nodes in this network, i.e. their
> local clustering is always between 0 and 2. Of course I could simply
> divide by two, but I would like to understand what is going wrong. I
> know that for directed/undirected network the normalization is
> different, hence I guess that something is not going on with the
> set_directed method.
This is because if you make the network undirected, it becomes a
multigraph, i.e. there are _two_ edges between 2 and 3. The clustering
coefficient is normalized only for _simple_ graphs, with at most one
edge between nodes.
If you want to transform the network into a multigraph, you can filter
the parallel edges out:
u = GraphView(g, efilt=logical_not(label_parallel_edges(g, mark_only=True).fa))
or remove them with remove_parallel_edges().
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