Alpha centrality

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In graph theory and social network analysis, alpha centrality is a measure of centrality of nodes within a graph. It is an adaptation of eigenvector centrality with the addition that nodes are imbued with importance from external sources.

Definition[edit]

Given a graph with adjacency matrix A_{i,j} the alpha centrality is defined as follows:

 \vec{x} = (I-\alpha A^T)^{-1}\vec{e} \,

where e_j is the external importance given to node j, and \alpha is a parameter.[1]

Motivation[edit]

To understand alpha centrality one must first understand Eigenvector Centrality. An intuitive process to compute eigenvector centrality is to give every node a starting random positive amount of influence. Each node then splits its influence evenly and divides it amongst its outward neighbors, receiving from its inward neighbors in kind. This process repeats until everyone is giving out as much as they're taking in and the system has reached steady state. The amount of influence they have at this steady state is their eigenvector centrality. Computationally this process is called the power method. We know that this process has converged when the vector of influence changes only by a constant as follows.

x_i = \frac{1}{\lambda} A^T_{i,j}x_j

Where x_i is the amount of influence that node i carries, A_{i,j} is the adjacency matrix and \lambda happens to be the principal eigenvalue (although this is not terribly important here).

Alpha centrality enhances this process by allowing nodes to have external sources of influence. The amount of influence that node i receives at every round is encoded in e_i. The process described above should now stop when

 x_i = \alpha A^T_{i,j}x_j + e_i \,

Where \alpha is a constant that trades off the importance of external influence against the importance of connection. When \alpha=0 only the external influence matters. When \alpha is very large then only the connectivity matters, i.e. we reduce to the eigenvector centrality case.

Rather than perform the iteration described above we can solve this system for x, obtaining the following equation:

 x = (I-\alpha A^T)^{-1}e \,

Applications[edit]

Alpha centrality is implemented in igraph library for network analysis and visualization.[2]

See also[edit]

Notes and references[edit]