For most modern-day data, graph data models are proving to be advantageous since they facilitate a diverse range of data analyses. This has spiked the interest and usage of graph databases, especially Neo4j. We study Neo4j and cypher along with various plugins that augment database capabilities in terms of data types or facilitate applications in data science and machine learning using plugins like graph data science (GDS).
A significant portion of the book is focused on discussing the structure and usage of graph algorithms. Readers will gain insights into well-known algorithms like shortest path, PageRank, or Label Propagation among others, and how one can apply these algorithms in real-world scenarios within a Neo4j graph.
Once readers become acquainted with the various algorithms applicable to graph analysis, we transition to data science problems. Here, we explore how a graph's structure and algorithms can enhance predictive modeling, prediction of connections in the graph, etc. In conclusion, we demonstrate that beyond its prowess in data analysis, Neo4j can be tweaked in a production setup to handle large data sets and queries at scale, allowing more complex and sophisticated analyses to come to life.
KEY FEATURES
* Utilizing graphs to improve search and recommendations on graph data models.
* Understand GDS and Neo4j graph algorithms including cluster detection, link prediction, and centrality.
* Complex problem-solving for predicting connections, application in ML pipelines and GNNs using graphs.
WHAT YOU WILL LEARN
* Understand Neo4j graphs and how to effectively query them with cypher.
* Learn to employ graphs for effective search and recommendations around graph data.
* Work with graph algorithms to solve problems like finding paths, centrality metrics, and detection of communities and clusters.
* Explore Neo4j’s GDS library through practical examples.
* Integrate machine learning with Neo4j graphs, covering data prep, feature extraction, and model training.
WHO THIS BOOK IS FOR
The book is intended to serve as a reference for data scientists, business analysts, graph enthusiasts, and database developers and administrators who work or intend to work on extracting critical insights from graph-based data stores.
Sonal Raj is an engineer, mathematician, data scientist, and a Python evangelist from India, who has carved a niche in the Financial Technology domain. He is a Goldman Sachs and D.E.Shaw alumnus who currently serves as a Vice President at a High frequency Trading firm, managing their Data Engineering and Research division.
Sonal holds dual masters in Computer Science and Business Management and is a former research fellow of the Indian Institute of Science. His areas of research range from image processing, and real-time graph computations to electronic trading algorithms and systems. Over the years, he has specialized in low-latency platforms, trading strategies, and market signal models. He is also a community speaker and a Python/Data Science mentor to newcomers in the field. Sonal is the author of the books Neo4j High Performance and The Pythonic Way.
When not engrossed in reading fiction or playing symphonies on the piano, he spends far too much time watching rockets lift off.
He is a loving son, husband, and custodian of his personal library.
