I came across a topic on computational linear algebra that talks about iterative algorithms to compute eigenvalues. I've worked with power method which is an iterative algorithm that converges a sequence of vectors to the largest eigenvalue.
One application of power method is the famous PageRank algorithm developed by Larry Page and Sergey Brin. The whole concept of this algorithm is an eigenvector problem corresponding to the largest eigenvalue of a system $Gv=v$ where $G$ is the Google matrix. This eigenvector can be found using the Power method.
Interestingly, I was wondering if PageRank has any application other than web surfing because it combines the concept of random walk and some computational graph theory and linear algebra which I suspect could have some applications in data science. Any idea is welcomed.