Hi Carl!
Let's look at this in 3-parts: graphs, their algorithms, and what that has to do to AI/ML.
1) Graphs:
Graphs are uniquely suited to manage/analyze connected data because they are, very simply, a mathematical representation of a network. The objects that make up graphs are called nodes (or vertices) and the links between them are called relationships (or edges).
2) Graph Algorithms:
Graph algorithms are built to operate on relationships and are exceptionally capable of finding structures and revealing
patterns in connected data. These algorithms calculate metrics based on the relationships between things. (So in other words, we would as or more interested in how many connections someone has and what type of relationships those are.) Graph algorithms serve us well when we need to understand structures and relationships to do things like forecast behavior, prescribe actions for dynamic groups, or find predictive components and patterns in our data.
3) AI/ML Fit:
Although only a few algorithms (like Label Propagation) are considered by some to be ML itself. However, improving the accuracy of machine learning predictions is a popular use of graph algorithms. We know that relationships are some of the strongest predictors of behavior. We also know that more information makes our ML models more predictive but that data scientists rarely have all the information they want to train on. Graph Algorithms helps us incorporate this highly predictive information (that we already have hidden in the data!) to increase the accuracy, precision, and recall of machine learning models.
People use graph algorithms for graph feature engineering to create scores that they can extract and use in their machine learning pipelines. In the last chapter of the
book, we walk through an example and compare the predictive quality of different models as we add more “graphy” features.