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## [igraph] Re: Example network

**From**: |
Tamas Nepusz |

**Subject**: |
[igraph] Re: Example network |

**Date**: |
Mon, 3 May 2010 23:31:57 +0100 |

Mike,
>* Thanks for taking the time to look at the weird eigenvector results that I *
>* get. Attached is an edgelist (undirected) that produces negative eigenvector *
>* centralities in R. Let me know if I can answer any other questions.*
Does your original graph have 817992 vertices or 1520? I get the former if I
try to load this graph as a standard edge list, so there are many isolated
vertices which do not really change anything but make the calculation somewhat
slower.
Anyway, I tried to load your graph both ways, and I also tried it from Python
instead of R. From Python, the minimum eigenvector centrality was -1.1842e-13
for the first case and -3.2782e-19 for the second. Both are within the
precision limits of the ARPACK solver, it is safe to consider these as zeros.
From R, the results were identical for the first case (when the graph had
817992 vertices), but I indeed got the negative eigenvalues for the second
case. Modifying ncv, nev or maxiter (as suggested by Gabor) did not seem to
help either. This is strange because so far we have only seen this behaviour
for pathological graphs, but yours does not seem like that at all.
Tomorrow I will try to take a closer look at the issue. Since the Python
interface gives correct results, I suspect there must be a difference between
the default ARPACK parameters used in Python and R, and maybe that will give us
a clue about what's going on here.
--
Tamas

**[igraph] Re: Example network**,
*Tamas Nepusz* **<=**