Graph-based machine learning: Part I

Many important problems can be represented and studied using graphs -- social networks, interacting bacterias, brain network modules, hierarchical image clustering and many more. If we accept graphs as a basic means of structuring and analyzing data about the world, we shouldn't be surprised to see them being widely used 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 a 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. So how can we extract this kind of information?