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.
Author(s): Dr. Alicia Frame, Zach Blumenfeld
Series: Learning made easy
Edition: 2
Publisher: Wiley
Year: 2023
Language: English
Pages: 53
INTRODUCTION................................................................................................ 1
About This Book.................................................................................... 1
Icons Used in This Book........................................................................ 2
Beyond the Book................................................................................... 2
CHAPTER 1: Understanding Graphs and
Graph Data Science..................................................................... 3
Explaining What a Graph Is.................................................................. 3
Defining Graph Analytics and Graph Data Science........................... 5
Looking at the Types of Questions for Graph Data Science............ 6
CHAPTER 2: Using Graph Data Science in the Real World......... 9
Looking at Graphs in the Health Industry........................................10
Discovering more efficient drugs.................................................10
Improving the patient journey.....................................................11
Recommendations and Personalized Marketing............................11
Fraud Detection...................................................................................12
CHAPTER 3: Evolving Your Use of Graph Data Science
Technology.......................................................................................13
Knowledge Graphs..............................................................................14
Graph Algorithms................................................................................15
Graph-Native Machine Learning.......................................................18
CHAPTER 4: Using Neo4j as a Graph Data
Science Platform.........................................................................21
Neo4j Graph Data Science.................................................................22
Neo4j Graph Database Management System.................................22
Neo4j Desktop and Browser..............................................................23
CHAPTER 5: Detecting Fraud with Graph Data Science.............25
Finding a Good Fraud Dataset...........................................................25
Removing Outliers...............................................................................26
Finding Suspicious Clusters...............................................................29
Visually Exploring a Suspicious Cluster............................................32
Using Graph Features to Predict Fraud............................................35
CHAPTER 6: Ten Tips with Resources for Successful
Graph Data Science...................................................................37
APPENDIX............................................................................................................41
Neo4j Bloom........................................................................................24
