Graph-based machine learning

Many important problems can be represented and studied using graph. If we accept graphs as a basic mean of structuring and analyzing data about the world, we shouldn't be surprised to see it being widely use in Machine Learning as a powerful tool that can enable intuitive properties and power a lot of useful features. Graph-based machine learning is destined to become this resilient piece of logic transcending a lot of other techniques. This post explores the tendencies of nodes in a graph to spontaneously form clusters of internally dense linkage (hereby termed community); a remarkable and almost universal property of biological networks. This is particularly interesting knowing that a lot of information can be extrapolated from a node's neighbor (e.g.