Название: Graph Data Science For Dummies, 2nd Neo4j Special EditionАвтор: Dr. Alicia Frame, Zach Blumenfeld
Издательство: For Dummies
Год: 2022
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
Connectivity is the single most pervasive characteristic of today’s networks and systems. From protein interactions to social networks, from communication systems to power grids, and from retail experiences to supply chains, networks with even a modest degree of complexity aren’t random, which means connections are neither evenly distributed nor static. Simple statistical analysis alone fails to sufficiently describe, let alone predict, behaviors within connected systems.
As the world becomes increasingly interconnected and systems increasingly complex, using technologies built to leverage relationships and their dynamic characteristics is imperative. Not surprisingly, interest in graph data science and graph analytics has exploded because they were explicitly developed to gain insights from connected data. Graph data science and graph analytics reveal the workings of intricate systems and networks at massive scale.
We are passionate about the utility and importance of Graph Data Science and graph analytics, so we wrote this book to help organizations better leverage graphs so they can make new discoveries and develop intelligent solutions faster.
In this book, we focus on the commercial applications of graph analysis and graph-enhanced Machine Mearning (ML), which takes the form of Graph Data Science. We also use Neo4j graph technology to illustrate a graph data science platform. You take a quick look at graph data science and its uses before covering the journey of graph data science adoption. You also review Neo4j technology as a graph data science platform and walk through a fraud detection example.
Graph approaches to data are exploding in the commercial world to better reveal meaning in data as well as forecast behavior of complex systems. This burst is due to the increasing connectedness of data, breakthroughs in scaling graph technology to enterprise-sized problems, excellent results when integrated with Machine Learning (ML) and Artificial Intelligence (AI) solutions, and more accessible tools for general analytics and Data Science teams.
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