# Implement Eigenvector Centrality

In eigenvector centrality, we must calculate the principal eigenvector of the network. In this respect, a node is central to the extent that its neighbors are central.

Using the adjacency matrix to find eigenvector centrality

For a given graph G:=(V,E) with |V| number of vertices let A = (a_{v,t}) be the adjacency matrix, i.e. a_{v,t} = 1 if vertex v is linked to vertex t, and a_{v,t} = 0 otherwise. The centrality score of vertex v can be defined as:

x_v = \frac{1}{\lambda} \sum_{t \in M(v)}x_t = \frac{1}{\lambda} \sum_{t \in G} a_{v,t}x_t

where M(v) is a set of the neighbors of v and \lambda is a constant. With a small rearrangement this can be rewritten in vector notation as the eigenvector equation

\mathbf{Ax} = {\lambda}\mathbf{x}

In general, there will be many different eigenvalues \lambda for which an eigenvector solution exists. However, the additional requirement that all the entries in the eigenvector be positive implies (by the Perronâ€“Frobenius theorem) that only the greatest eigenvalue results in the desired centrality measure.[19] The v^{th} component of the related eigenvector then gives the centrality score of the vertex v in the network. Power iteration is one of many eigenvalue algorithms that may be used to find this dominant eigenvector.[18] Furthermore, this can be generalized so that the entries in A can be real numbers representing connection strengths, as in a stochastic matrix.

## Blueprint information

- Status:
- Started

- Approver:
- Dimitris Kalamaras

- Priority:
- Essential

- Drafter:
- Dimitris Kalamaras

- Direction:
- Approved

- Assignee:
- Dimitris Kalamaras

- Definition:
- Approved

- Series goal:
- Accepted for 2.x

- Implementation:
- Deployment

- Milestone target:
- 2.2

- Started by
- Dimitris Kalamaras on 2017-01-23

- Completed by