# Eigenvector Centrality of a Graph

Given a graph with adjacency matrix $\mathbf A$, the eigenvector centrality is

$$ \mathbf e_u = \frac{1}{\lambda} \sum_{v\in\mathcal V} \mathbf A[u,v] \mathbf e_v, \qquad \forall u \in \mathcal V. $$

## Power Iteration

The solution to $\mathbf e$ is the eigenvector that corresponds to the largest eigenvalue $\lambda_1$. Power iteration method can help us get this eigenvector, i.e., the $^{(t+1)}$ iteration is related to the previous iteration $^{(t)}$, through the following relation,

$$ \mathbf e^{(t+1)} = \mathbf A \mathbf e^{(t)}. $$

If we set $\mathbf e^{(0)} \to (1, 1, \cdots , 1)$.

## Indications of the Power Iteration Method

The power iteration method hints that the eigenvector centrality is related to walking through the graph.

For step 0, we have

$$ e^{(1)}_i = A_{ij} e^{(0)}_j, $$

$e^{(0)} _ j$ is the probability of visiting node $j$, which is 1. $A_{ij}$ is the transfer probability from node $j$ to $i$. After this iteration, we get the number of visits on node $i$.

Repeat this, we have a vector that shows the number of visits on node $i$ after $t+1$ steps.

That being said, the eigenvector centrality is proportional to the likelihood of visiting the nodes after infinite steps of random walks on the graph.

L Ma (2021). 'Eigenvector Centrality of a Graph', Datumorphism, 09 April. Available at: https://datumorphism.leima.is/cards/graph/graph-eigenvector-centrality/.