Re: [igraph] threshold

[Gábor Csárdi]

Then keep adding edges to the 0.2% graph until the graph gets connected.

G.

On Tue, Mar 30, 2010 at 5:08 PM, Harun Pirim <address@hidden> wrote:
> Thank you very much,
> Another question arises about connectivity.
> How can I guarantee connectivity for this binary graph, in other words use
> determine a threshold optimizing connectivity, strongest 0.2% edges (if
> violates connectivity then less stronger edge is chosen) in R?
>
> -----Original Message-----
> From: address@hidden
> [mailto:address@hidden On Behalf Of
> Gábor Csárdi
> Sent: Tuesday, March 30, 2010 2:59 AM
> To: Help for igraph users
> Subject: Re: [igraph] threshold
>
> On Sun, Mar 28, 2010 at 4:30 PM, Tamas Nepusz <address@hidden> wrote:
>> Hi,
>>
>>> Is there a direct way in iGraph to calculate a threshold that will retain
> let's say 2% of the strongest weighted edges and convert a weighted graph to
> a binary graph using this threshold ?
>> No, since it is only a few lines of code using either the R or the Python
> interface. In Python, you can do something like this:
>>
>> weights = sorted(g.es["weight"], reverse=True)
>> threshold = weights[len(weights) * 0.02]
>> g.delete_edges(g.es.select(weight_lt = threshold))
>> del g.es["weight"]
>>
>> --
>> Tamas
> [...]
>
> The same in R:
>
> weights <- sort(E(g)$weight, decreasing=TRUE)
> threshold <- weights[length(weights) * 0.02]
> g2 <- delete.edges(g, which(E(g)$weight < threshold)-1)
> g2 <- remove.edge.attribute(g, "weight")
>
> Gabor
>
> --
> Gabor Csardi <address@hidden>     UNIL DGM
>
>
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-- 
Gabor Csardi <address@hidden>     UNIL DGM