Why Graph Theory Is Cooler Than You Thought

Talk to a scientist in just about any discipline, and ask them the question -- based on their discipline -- "how does that stuff work?" You'll likely find that there are systems and networks that you have to consider before you can really understand how any given thing works: whether that's the human body, a food chain in an ecosystem, a chemical reaction, or a society as a whole. Without understanding the relationship between two animals in an ecosystem, two atoms in a molecule, or cells and tissues in our body, you just have a bunch of data: a list of cells, a readout of animals and what they eat, etc. Traditional machine learning models often take data this way: they take lists or tables of data and do some stuff (the details of which depend on the algorithm being used as well as a few other parameters) to make predictions about a thing. If this in-depth educational content is useful for you, subscribe to our AI research mailing list to be alerted when we release new material. Graphs are data structures that represent networks of, or relationships between the data they contain. Typically, they're represented as "nodes" and lines, or "edges".