This week my job was to start figuring out how to use PageRank and to find other ways to find the probability of relation. I started by looking at the algorithm for PageRank. Here is a picture that helps explain how it works:
PageRank is a link analysis algorithm that gives each link a numerical weighting to each element of a hyperlinked set of documents. The purpose is to measure its relative importance within the set.
I also looked into Low-Rank Matrix Factorization, but due to problems concerning speed and size, my research on that topic has become moot.
Next week I will continue working on PageRank. I will also start working on understanding the influence of all nodes in a network and efficient collective influence maximization in cascading processes with first-order transitions. After I obtain an adequate understanding of those things, I will need to code the algorithm or find the algorithm already coded in Python.