"A generalized eigenvector centrality for multilayer networks with inter-layer constraints on adjacent node importance" by H. Robert Frost
Visualization of constrained eigenvector centrality for a 2 layer network with varying inter-layer dependency structures.
Each row corresponds to a separate layer generated as a interconnected group of 5 Erdos-Renyi random graphs that each have 20 nodes.
The left column visualizes node eigenvector centrality when the layers are independent.
The middle column visualizes node eigenvector centrality when 10% of the importance of adjacent nodes is based on the other layer.
The left column visualizes the node eigenvector centrality when adjacent node importance is entirely based on the other layer.
CMLC_SimpleExample.pdf:Vignette illustrating use of the CMLC R package to compute the constrained eigenvector centrality values for a simple 3 layer network. This replicates the results in Section 3.1 of the paper.
CMLC_RandomGraphExample.pdf:Vignette illustrating use of the CMLC R package to compute the constrained eigenvector centrality values for a two layer random graph network. This replicates the results in Section 3.2 of the paper and is the model associated with the above figure.
Contact
For more information regarding the paper or CMLC R package, contact: H. Robert Frost (email: rob dot frost at dartmouth dot edu)
