Eigenvector Centrality of a Graph
#Graph #Statistics
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/grapheigenvectorcentrality/.
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