# eigenvector#

graph_tool.centrality.eigenvector(g, weight=None, vprop=None, epsilon=1e-06, max_iter=None)[source]#

Calculate the eigenvector centrality of each vertex in the graph, as well as the largest eigenvalue.

Parameters:
gGraph

Graph to be used.

weightEdgePropertyMap (optional, default: None)

Edge property map with the edge weights.

vpropVertexPropertyMap, optional (default: None)

Vertex property map where the values of eigenvector must be stored. If provided, it will be used uninitialized.

epsilonfloat, optional (default: 1e-6)

Convergence condition. The iteration will stop if the total delta of all vertices are below this value.

max_iterint, optional (default: None)

If supplied, this will limit the total number of iterations.

Returns:
eigenvaluefloat

The largest eigenvalue of the (weighted) adjacency matrix.

eigenvectorVertexPropertyMap

A vertex property map containing the eigenvector values.

betweenness

betweenness centrality

pagerank

PageRank centrality

hits

authority and hub centralities

trust_transitivity

pervasive trust transitivity

Notes

The eigenvector centrality $$\mathbf{x}$$ is the eigenvector of the (weighted) adjacency matrix with the largest eigenvalue $$\lambda$$, i.e. it is the solution of

$\mathbf{A}\mathbf{x} = \lambda\mathbf{x},$

where $$\mathbf{A}$$ is the (weighted) adjacency matrix and $$\lambda$$ is the largest eigenvalue.

The algorithm uses the power method which has a topology-dependent complexity of $$O\left(N\times\frac{-\log\epsilon}{\log|\lambda_1/\lambda_2|}\right)$$, where $$N$$ is the number of vertices, $$\epsilon$$ is the epsilon parameter, and $$\lambda_1$$ and $$\lambda_2$$ are the largest and second largest eigenvalues of the (weighted) adjacency matrix, respectively.

Parallel implementation.

If enabled during compilation, this algorithm will run in parallel using OpenMP. See the parallel algorithms section for information about how to control several aspects of parallelization.

References

[langville-survey-2005]

A. N. Langville, C. D. Meyer, “A Survey of Eigenvector Methods for Web Information Retrieval”, SIAM Review, vol. 47, no. 1, pp. 135-161, 2005, DOI: 10.1137/S0036144503424786 [sci-hub, @tor]

L. A. Adamic and N. Glance, “The political blogosphere and the 2004 US Election”, in Proceedings of the WWW-2005 Workshop on the Weblogging Ecosystem (2005). DOI: 10.1145/1134271.1134277 [sci-hub, @tor]

Examples

>>> g = gt.collection.data["polblogs"]
>>> g = gt.GraphView(g, vfilt=gt.label_largest_component(g))
>>> w = g.new_edge_property("double")
>>> w.a = np.random.random(len(w.a)) * 42
>>> ee, x = gt.eigenvector(g, w)
>>> gt.graph_draw(g, pos=g.vp["pos"], vertex_fill_color=x,
...               vertex_size=gt.prop_to_size(x, mi=5, ma=15),
...               vcmap=matplotlib.cm.gist_heat,
...               vorder=x, output="polblogs_eigenvector.pdf")
<...>