Can you simply print the values given by the following?

cluster = gt.local_clustering(g)
loc_cluster = [c for c in cluster]
print(loc_cluster)

In my opinion, this is the correct way to calculate local clustering
coefficients.

Regards
Snehal

On Wed, Oct 19, 2016 at 5:15 PM, P-M <[email protected]> wrote:

> I have computed the local clustering coefficient for my network and then
> created a histogram as follows:
>
> g = gt.load_graph('graph_no_multi.gt')
>
> #"The clustering coefficient is normalized only for _simple_ graphs, with
> at
> #most one edge between nodes."
> gt.remove_parallel_edges(g)
>
> #create new property map
> clustering = g.new_vertex_property("float")
>
> #calculate clustering coefficient
> gt.local_clustering(g,prop=clustering,undirected=False)
>
> #Make propery map internal
> g.vp.clust = clustering
> #Initiliase dictionary containing list of clustering coefficients for given
> degree
> clust_k_hist={}
> for v in g.vertices():
>     k = v.out_degree()
>     c=g.vp.clust[v]
>     if k in clust_k_hist:
>         clust_k_hist[k].append(c)
>     else:
>         clust_k_hist[k]=[c]
>
> If I now however type `print max(clust_k_hist[0])` I get an answer of `2`
> which surprises me slightly (similarly for `k=1` I get a value of `2`).
> Firstly I wasn't expecting a clustering coefficient greater than `1` but
> also for a degree of `0` I would expect to find only clustering
> coefficients
> of `0`. The documentation states that the outdegree is being used in
> calculating the local clustering coefficient so I am using the outdegree
> for
> compiling the histogram.
>
> Have I gone wrong somewhere?
>
>
>
> --
> View this message in context: http://main-discussion-list-
> for-the-graph-tool-project.982480.n3.nabble.com/Local-
> Clustering-Coefficient-for-k-1-tp4026696p4026772.html
> Sent from the Main discussion list for the graph-tool project mailing list
> archive at Nabble.com.
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>



-- 




Snehal Madhukar Shekatkar
Pune
India
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