Harnessing graph-native algorithms to enhance machine learning: A primer

Description

Graph databases have become much more widely popularized in the recent year. Brandy Freitas demystifies the mathematical principles behind graph databases, offers a primer to graph native algorithms, and outlines the current use of graph technology in industry.

By representing highly connected data in a graph, you have access to a host of graph native algorithms that depend on and exploit the relationships between your data. Computing and storing graph metrics can add strong new features to nodes, creating innovative predictors for machine learning. Using algorithms designed for path finding, centrality, community detection, and graph pattern matching, you can rely less on inflexible, subject-driven feature engineering.

Beyond use of derived graph metrics, finding a way to incorporate information about the structure of the graph is a critical issue for furthering the use of machine learning on connected data. So, the question is how to enable the machine learning algorithm to access the inherent structure of the graph itself. Similar to the movement in natural language processing (Word2Vec), where the aim is to preserve information about where a word is in a sequence, there’s a movement in graph analysis to capture community and adjacency of nodes. Using node embedding to create a low dimension vector representation of the node and its structural components, you no longer need to compromise and query away important structural relationships.

You’ll leave with an understanding of the uses of native graph algorithms, advantages to using graph derived metrics in feature engineering, and current techniques for encoding graph structural information into low dimensional feature vectors.