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