I have a simple graph with a few components (less than 30 nodes in total).

When I calculate a simple eigenvector centrality measure (as in the

documentation), x[0] = inf and all the values in x[1].a = 06666667. I was

wondering whether I'm missing something or if there is an explanation for

why this is the case? Thank you!

Could you please provide a graph where this problem occurs?

Cheers,

Tiago

Graph in GraphML

<http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/file/n4024784/example_graph.graphml>

Graph in PDF

<http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/file/n4024784/example_graph.pdf>

Hi, thank you for the reply. Attached are two files: a graph in GraphML

format, and a PDF of the graph. It's a very simple graph, but similar to

what I described. In this case x[1].a = 0.03571429 and x[0] = inf.

Hi,

Great, glad I could have helped! Before I jump to the git version, I'm

curious as to whether graph-tool can calculate eigenvector centrality for

signed networks. Bonacich and Lloyd suggest a eigenvector measure on a

symmetrical adjacency matrix to infer status (2004)

<http://www.sciencedirect.com/science/article/pii/S0378873304000449> . That

is to say, an edge signed with -1 represents a negative relationship, and an

edge signed with 1 represents a positive relationship, and thus those with

negative and positive eigenvector centralities belong in different

"cliques". Is it possible to arrive at this idea through the eigenvector

centrality implemented in graph-tool if one were to assign -1 or 1 as edge

weights in the graph? I.e., does the implemented algorithm take into account

negative vs. positive weights? Sorry to jump the gun here, but the

extensibility of graph-tool to more obscure measures is intriguing...

This should work without a problem. The function in graph-tool

implements the power method, which works for any matrix, and always

converges as long as the largest eigenvalue is non-degenerate.

Cheers,

Tiago