PageRank Algorithm Applied To the Food Web

An anonymous reader brings word of a new application for PageRank, Google's link analysis algorithm: monitoring the food web in an ecosystem. A team of researchers found that a modified version of PageRank can predict with great accuracy which species are vital to the existence of others. Quoting: "Every species is embedded in a complex network of relationships with others. A single extinction can cascade into the loss of seemingly unrelated species. Investigating when this might happen using more conventional methods is complicated, as even in simple ecosystems, the number of combinations exceeds the number of atoms in the universe. So, it would be impossible to try them all. Co-author Dr. Stefano Allesina realized he could apply PageRank to the problem when he stumbled across an article in a journal of applied mathematics describing the Google algorithm. 'First of all, we had to reverse the definition of the algorithm. In PageRank, a web page is important if important pages point to it. In our approach, a species is important if it points to important species.'"

Comments

Re:Why is this surprising?

[jpmorgan]

Yes, it is notable that this wasn't published in 'some mathematics journal'. Pagerank is computing the limiting distribution of a discrete-time markov chain applied to webpages using a certain statistical model of hyperlinks. It makes no sense to talk of applying 'pagerank' to things other than webpages, because that's what makes pagerank special! As soon as you take pagerank out of the web-context, it's just a steady state analysis of a markov chain, which is a standard statistical technique covered in undergraduate statistics courses. It's like saying applying bayesian inference to a problem in ecology is using a 'spam filter.'

For me, this tells me that perhaps these researchers should wander over to their local mathematics department more often. They might find all sorts of goodies that mathematicians have developed in the past few centuries. Dr. Allesina might have discovered that there was no need to reverse engineer the algorithm, since the underlying mathematical principles have been well understood for over a hundred years. We might have a better understanding of the world if most sciences took mathematical models as seriously as physicists do.