Re: [igraph] Negative eigenvector centrality?

[Nathan Cobb]

Perfect, thanks!

- Nate


On 10/22/09 11:49 AM, "Gábor Csárdi" <address@hidden> quoth:

> Nathan,
> 
> the eigenvector centrality is defined as the eigenvector of a matrix,
> and the opposite of the eigenvector is also an eigenvector. igraph
> 0.5.2 does not care about the sign of the centrality score, just
> returns the result of the eigenvector computation. If you get negative
> scores you can just multiply them by -1. As long as either _all_
> scores are non-negative or _all_ scores are non-positive, there is no
> bug here.
> 
> igraph 0.6 makes sure that always a positive vector is returned,
> exactly the way suggested above, it just multiplies by -1 if the
> scores are negative.
> 
> Best,
> Gabor
> 
> On Thu, Oct 22, 2009 at 5:38 PM, Nathan Cobb <address@hidden> wrote:
>> I'm working with a series of large, sparse communication networks
>> (v~7-15,00;e~10-30,000) The networks are initially directed but not
>> weighted.
>> 
>> Using iGraph 0.52 with the Python interface I'm getting back negative
>> Eigenvector centrality scores no matter what I do. The code looks something
>> like:
>> 
>> target_graph.add_vertices(len(vertices))
>> target_graph = target_graph.add_edges(edges)
>> target_graph.to_undirected()
>> target_graph.simplify(multiple=False,loops=True)
>> eigenvector_centrality = target_graph.eigenvector_centrality(scale=False)
>> 
>> The resulting values seem to range somewhere between -1 and 0 (never
>> positive.)
>> 
>> Am I missing something conceptually here, or am I hitting a bug?
>> 
>> Thanks,
>> 
>> - Nate
>> 
>> 
>> Nathan Cobb, MD | Research Investigator
>> T 202-454-5745  F 202-454-5785
>> Schroeder Institute for Tobacco Research and Policy Studies
>> American Legacy Foundation
>> 1724 Massachusetts Avenue, NW, Washington, DC 20036
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
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>> 
> 
>