Reveal Hidden Patterns in Data and Enhance Machine Learning Predictions Using Apache Spark & Neo4j
Neo4j, the leader in graph database technologies, announced the launch of Graph Algorithms: Practical Examples in Apache Spark & Neo4j, published by O’Reilly Media.
The book, co-authored by graph technology experts Mark Needham and Amy E. Hodler, delivers applicable examples in Apache Spark and the Neo4j database.
Co-author Amy E. Hodler, an expert in network science who serves as graph analytics and AI program manager at Neo4j, explained why graph analytics and algorithms are an important consideration for application development.
“Because graph algorithms use the connections inherent within data, they reveal structures that other analytic techniques miss,” said Hodler. “Organizations that don’t make use of the rich and evolving relationships within data will be left behind. Graph algorithms provide one of the most potent approaches to analyzing connected data because their mathematical calculations are specifically built to operate upon and follow relationships.”
Mark Needham, co-author of Graph Algorithms and software architect and engineer at Neo4j, explained why the time is right for application developers to know more about graph analytics and specifically graph algorithms.
“Until recently, adopting graph analytics required significant expertise and determination,” said Needham. “Setbacks included a lack of tooling and complex integration. Few knew how to apply graph algorithms to their existing questions. It is our goal to help change this.”
As data becomes increasingly interconnected and systems increasingly sophisticated, Kirk Borne, Ph.D., Principal Data Scientist and Executive Advisor at Booz Allen Hamilton, shared why he found Graph Algorithms valuable.
“From basic concepts to fundamental algorithms to processing platforms and practical use cases, the authors have compiled an instructive and illustrative guide to the wonderful world of graphs,” said Dr. Borne.
Whether building dynamic network models, mitigating risk and fraud or forecasting real-world behavior, the book illustrates how graph algorithms deliver value – from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions.
Readers will understand how and when to apply graph algorithms – including PageRank, Label Propagation and Louvain Modularity – in addition to learning how to create a machine learning workflow for link prediction that combines Neo4j and Spark. Working code and sample data sets from both Spark and Neo4j are included to ensure concepts are understood and can be applied.
Andy Jefferson, Neural Networks and Graph AI Researcher at Octavian.ai, remarked on the practical examples shared in Graph Algorithms.
“A wide range of algorithms for analyzing and understanding graphs are covered,” said Jefferson. “The combination of clear explanations and working code samples make it easy to follow how each algorithm works. The versatility of graph algorithms is shown by the diverse selection of realistic scenarios used as examples in this book.”