The world is driven by connections--from financial and communication systems to social and biological processes. As connectedness continues to accelerate, it's not surprising that interest in graph algorithms has exploded because they are based on mathematics explicitly developed to gain insights from the relationships between data. Graph analytics can uncover the workings of intricate systems and networks at massive scales--for any organization. We are passionate about the utility and importance of graph analytics as well as the joy of uncovering the inner workings of complex scenarios. Until recently, adopting graph analytics required significant expertise and determination, because tools and integrations were difficult and few knew how to apply graph algorithms to their quandaries.

In 2019, I was asked to write the Foreword for the book "Graph Algorithms: Practical Examples in Apache Spark and Neo4j", by Mark Needham and Amy E. Hodler. I wrote an extensive piece on the power of graph databases, linked data, graph algorithms, and various significant graph analytics applications. In their wisdom, the editors of the book decided that I wrote "too much". So, they correctly shortened my contribution by about half in the final published version of my Foreword for the book. The book is awesome, an absolute must-have reference volume, and it is free (for now, downloadable from Neo4j).

Learn more about how graph algorithms can help leverage relationships within data in the recently published book "Graph Algorithms: Practical Examples in Apache Spark and Neo4j," by Mark Needham and Amy E. Hodler. That's why we work on graph technologies, which help people make use of these connections. It's also why we wrote the O'Reilly book: Graph Algorithms: Practical Examples in Apache Spark and Neo4j. Simply put, a graph is a mathematical representation of any type of network. The objects that make up graphs are called nodes (or vertices) and the links between them are called relationships (or edges.)