Applying graph theory in new domains
In recent years, due to there being a more solid theoretical understanding of graph machine learning, as well as an increase in available storage space and computational power, we can identify a number of domains in which such learning theories are spreading. With a bit of imagination, you can start looking at the surrounding world as a set of "nodes" and "links." Our work or study place, the technological devices we use every day, and even our brain can be represented as networks. In this section, we will look at some examples of how graph theory (and graph machine learning) has been applied to, apparently, unrelated domains.
Graph machine learning and neuroscience
The study of the brain by means of graph theory is a prosperous and expanding field. Several ways of representing the brain as a network have been investigated, with the aim of understanding how different parts of the brain (nodes) are functionally or structurally...
