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## Re: [igraph] Help with Weighted_Adjacency

**From**: |
Tamás Nepusz |

**Subject**: |
Re: [igraph] Help with Weighted_Adjacency |

**Date**: |
Wed, 25 Jun 2014 15:34:41 +0200 |

>* graph3 = Graph.Weighted_Adjacency(AdjacencyMatrix3, mode = 'directed', attr = *
>* 'weight')*
>* evcentr3 = Graph.evcent(graph3) *
You need to specify that evcent() should use the weights coming from the
"weight" attribute; see help(Graph.evcent)
@param weights: edge weights given as a list or an edge attribute. If
C{None}, all edges have equal weight.
So you need this:
evcentr3 = graph3.evcent(weights=graph3.es["weight"])
The same applies for pagerank() and betweenness(). However, note that the
semantics of weights is different for the betweenness centrality; for PageRank
and eigenvector centrality, a larger weight means a closer association between
the endpoints, while this is exactly the opposite for betweenness() since it
works with _shortest_ paths.
>* I am using python and have aldo had problems with the following commands:*
>* *
>* graph3.is_weighted*
>* AttributeError: 'Graph' object has no attribute 'is_weighted'*
is_weighted() is not an attribute but a method:
graph3.is_weighted()
>* g = Graph.Adjacency(AdjacencyMatrix3, weighted = True)*
>* TypeError: 'weighted' is an invalid keyword argument for this function*
Adjacency() does not have a keyword argument named "weighted"; see
help(Graph.Adjacency)
T.