Graph Data Science (GDS) For Dummies®, Neo4j Special Edition (Amy Hodler Mark Needham) (Z-Library)

These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Graph Data Science (GDS) Neo4j Special Edition by Amy Hodler and Mark Needham These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Graph Data Science (GDS) For Dummies®, Neo4j Special Edition Published by John Wiley & Sons, Inc. 111 River St. Hoboken, NJ 07030-5774 www.wiley.com Copyright © 2021 by John Wiley & Sons, Inc. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. Trademarks: Wiley, For Dummies, the Dummies Man logo, The Dummies Way, Dummies.com, Making Everything Easier, and related trade dress are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries, and may not be used without written permission. Neo4j and the Neo4j logo are registered trademarks of Neo4j. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc., is not associated with any product or vendor mentioned in this book. LIMIT OF LIABILITY/DISCLAIMER OF WARRANTY: THE PUBLISHER AND THE AUTHOR MAKE NO REPRESENTATIONS OR WARRANTIES WITH RESPECT TO THE ACCURACY OR COMPLETENESS OF THE CONTENTS OF THIS WORK AND SPECIFICALLY DISCLAIM ALL WARRANTIES, INCLUDING WITHOUT LIMITATION WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE. NO WARRANTY MAY BE CREATED OR EXTENDED BY SALES OR PROMOTIONAL MATERIALS.  THE ADVICE AND STRATEGIES CONTAINED HEREIN MAY NOT BE SUITABLE FOR EVERY SITUATION. THIS WORK IS SOLD WITH THE UNDERSTANDING THAT THE PUBLISHER IS NOT ENGAGED IN RENDERING LEGAL, ACCOUNTING, OR OTHER PROFESSIONAL SERVICES.  IF PROFESSIONAL ASSISTANCE IS REQUIRED, THE SERVICES OF A COMPETENT PROFESSIONAL PERSON SHOULD BE SOUGHT. NEITHER THE PUBLISHER NOR THE AUTHOR SHALL BE LIABLE FOR DAMAGES ARISING HEREFROM. THE FACT THAT AN ORGANIZATION OR WEBSITE IS REFERRED TO IN THIS WORK AS A CITATION AND/OR A POTENTIAL SOURCE OF FURTHER INFORMATION DOES NOT MEAN THAT THE AUTHOR OR THE PUBLISHER ENDORSES THE INFORMATION THE ORGANIZATION OR WEBSITE MAY PROVIDE OR RECOMMENDATIONS IT MAY MAKE.  FURTHER, READERS SHOULD BE AWARE THAT INTERNET WEBSITES LISTED IN THIS WORK MAY HAVE CHANGED OR DISAPPEARED BETWEEN WHEN THIS WORK WAS WRITTEN AND WHEN IT IS READ. For general information on our other products and services, or how to create a custom For Dummies book for your business or organization, please contact our Business Development Department in the U.S. at 877-409-4177, contact info@dummies.biz, or visit www.wiley.com/go/custompub. For information about licensing the For Dummies brand for products or services, contact Branded Rights&Licenses@Wiley.com. ISBN: 978-1-119-74604-1 (pbk); ISBN: 978-1-119-74605-8 (ebk) Manufactured in the United States of America 10 9 8 7 6 5 4 3 2 1 Publisher’s Acknowledgments Some of the people who helped bring this book to market include the following: Project Manager: Carrie Burchfield-Leighton Sr. Managing Editor: Rev Mengle Acquisitions Editor: Ashley Coffey Production Editor: Siddique Shaik Business Development Representative: Molly Daugherty These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Table of Contents iii Table of Contents 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 .......................... 6 Looking at the Types of Questions for GDS ...................................... 6 CHAPTER 2: Using Graph Data Science in the Real World ........ 9 Looking at Graphs in Healthcare ...................................................... 10 Discovering more efficient drugs ................................................ 10 Improving the patient journey .................................................... 11 Recommendations and Personalized Marketing ........................... 11 Fraud Detection .................................................................................. 12 CHAPTER 3: Evolving Your Application of GDS Technology .......................................................................... 13 Knowledge Graphs ............................................................................. 14 Graph Analytics ................................................................................... 15 Graph Feature Engineering ............................................................... 17 Graph Embedding .............................................................................. 18 Graph Networks ................................................................................. 19 CHAPTER 4: Using Neo4j as a Graph Data Science Platform ........................................................................ 21 Neo4j GDS Library .............................................................................. 21 Neo4j Graph Database Management System ................................ 22 Neo4j Desktop and Browser ............................................................. 23 Neo4j Bloom ....................................................................................... 24 Graph Data Science (GDS) These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

iv Graph Data Science (GDS) For Dummies, Neo4j Special Edition CHAPTER 5: Detecting Fraud with Graph Data Science ............ 25 Finding a Good Fraud Dataset .......................................................... 25 Removing Outliers .............................................................................. 26 Finding Suspicious Clusters............................................................... 28 Visually Exploring a Suspicious Cluster ........................................... 32 Predicting Fraudsters Using Graph Features .................................. 35 CHAPTER 6: Ten Tips with Resources for Successful Graph Data Science .................................................................. 37 APPENDIX .......................................................................................................... 41 These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

Introduction 1 Introduction 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 rela- tionships and their dynamic characteristics is imperative. Not surprisingly, interest in graph data science (GDS) and graph ana- lytics has exploded because they were explicitly developed to gain insights from connected data. GDS and graph analytics reveal the workings of intricate systems and networks at massive scale. About This Book We are passionate about the utility and importance of GDS and graph analytics, so we wrote this book to help organizations bet- ter 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 learning (ML), which takes the form of GDS. We also use the Neo4j graph technology to illustrate a GDS platform. You take a quick look at GDS and its uses before covering the journey of GDS adoption. You also review Neo4j technology as a GDS platform and walk through a fraud detection example. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

2 Graph Data Science (GDS) For Dummies, Neo4j Special Edition Icons Used in This Book The following icons are used in this book: Information here can be filed away for later use. This information may not be critical to most people, but if you like the extra techie tidbits, you’ll enjoy the insight here. Otherwise, just skip over it! Are you interested in saving time or effort on your projects? Check out these tips to help you do just that. Beyond the Book This book is focused on GDS and relies on graph theory, graph analytics, and graph databases. If you want resources beyond what we can offer you in this short book, we recommend the following: » neo4j.com/graph-algorithms-book: For hands-on graph algorithm examples, this book provides usable code and explanations for getting started. » neo4j.com/graph-databases-book: Additional detail on the Neo4j graph database and its property graph model can be found here. » neo4j.com/graph-databases-for-dummies: If you’re new to graph databases, this book is a great place to start your journey because it assumes no previous experience and walks you through modeling, querying, and importing graph data, all the way through your first production system. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

CHAPTER 1 Understanding Graphs and Graph Data Science 3 Chapter 1 IN THIS CHAPTER » Defining a graph » Understanding graph analytics and GDS » Using questions to explore GDS Understanding Graphs and Graph Data Science 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. In this chapter, you discover how we define a graph and the rela- tionship of graphs to analytics and data science. You also get a foundation in how graphs are used to answer tough questions about complex systems. Explaining What a Graph Is Networks are a representation, a tool to understand complex sys- tems and the complex connections inherent in today’s data. For example, you can represent how a social system works by think- ing about interactions between pairs of people. By analyzing the These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

4 Graph Data Science (GDS) For Dummies, Neo4j Special Edition structure of this representation, you can answer questions and make predictions about how the system works or how individ- uals behave within it. In this sense, network science is a set of technical tools applicable to nearly any domain, and graphs are the mathematical models used to perform analysis. Simply put, graphs are a mathematical representation of complex systems. Graphs have a history dating back to 1736. The origins of graph theory hail from the city of Königsberg, which included two large islands connected to each other and the two mainland portions of the city by seven bridges. The puzzle was to create a walk through the city, crossing each bridge once and only once. Leonhard Euler solved that puzzle by asking whether it was possible to visit all four areas of a city connected by seven bridges, while only cross- ing each bridge once. It wasn’t. With the insight that only the connections themselves were relevant to solving this kind of problem, Euler established the groundwork for graph theory and its mathematics. As one of Euler’s original sketches, Figure 1-1 depicts Euler’s progression: » Walking the bridges of Königsberg: Four main areas of Königsberg with seven bridges. Can you cross each bridge only once and return to your starting point? » Euler’s insight: The only relevant data is the main areas and the bridges connecting them. » Origins of graph theory: Euler abstracted the problem and created generalized rules based on nodes and relationships that apply to any connected system. FIGURE 1-1: The origins of graph theory. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

CHAPTER 1 Understanding Graphs and Graph Data Science 5 While graphs originated in mathematics, they are also a prag- matic and faithful representation of data for modeling and anal- ysis. A graph is a representation of a network, often illustrated with circles to represent entities, also called nodes or vertices, and lines between them. Those lines are known as relationships, links, or edges. Think of nodes as the nouns in sentences, and relation- ships as verbs that give context to the nodes. To avoid any con- fusion, the graphs we talk about in this book have nothing to do with graphing equations or charts. Take a look at the differences in Figure 1-2. The bottom graph on the left in Figure  1-2 is a person graph. When looking at that graph, you can construct several sentences to describe it. For example, person A lives with person B who owns a car, and person A drives a car that person B owns. This model- ing approach maps easily to the real world and is whiteboard- friendly, which helps align data modeling and analysis. We often use the phrase “whiteboard-friendly” for anything that’s easy to describe with simple drawings that you could illus- trate on a whiteboard. FIGURE 1-2: A graph is a representation of a network. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

6 Graph Data Science (GDS) For Dummies, Neo4j Special Edition Defining Graph Analytics and Graph Data Science Modeling graphs is only half of the story. You may also want to analyze them to reveal insight that isn’t immediately obvious. So in this section, we explain the domain of graph data science (GDS) and graph analytics. GDS is a science-driven approach to gain knowledge from the relationships and structures in data, typically to power predic- tions. It uses multi-disciplinary workflows that may include que- ries, statistics, algorithms, and ML. GDS can typically be broken down into three areas: » Graph statistics provides basic measures about a graph, such as the number of nodes and distribution of relation- ships. These insights may influence how you configure and execute more complex analysis as well as interpret results. » Graph analytics builds on graph statistics by answering specific questions and gaining insights from connections in existing or historical data. Graph queries and algorithms are typically applied together in “recipes” during graph analytics, and the results are used directly for analysis. » Graph-enhanced ML and AI is the application of graph data and analytics results to train ML models or support probabi- listic decisions within an AI system. Graph statistics and analytics are often used in conjunction to answer certain types of questions about complex systems and the subsequent insights, applied to improve ML. Looking at the Types of Questions for GDS Data scientists try to tackle many types of questions when using GDS to evaluate interdependencies, infer meaning, and predict behavior. At the most abstract level, these questions fall into a few broad areas: movement, influence, groups and interactions, and patterns, as shown in Figure 1-3. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

CHAPTER 1 Understanding Graphs and Graph Data Science 7 The areas in Figure 1-3 answer the following questions: » How do things travel (move) through a network? Understanding how things move through a network involves deep path analysis to find propagation pathways, such as the route of diseases or network failures. It can also be used to optimize for the best possible route or for flow con- straints. We cover these classic uses for pathing algorithms more in Chapter 3. » What are the most influential points? Identifying influenc- ers involves uncovering the structurally well-placed nodes that represent the control points in a network. These influencers can act as fast dissemination points, bridges between less connected groups, or bottlenecks. Influencers can accelerate or slow the flow of items through networks from finances to opinions. The concept of highly connected and influential nodes in a graph is referred to as centrality. Centrality algorithms are essential for understanding influence in a network. » What are the groups and interactions? Detecting commu- nities requires grouping and partitioning nodes based on the number and strength of interactions. This method is the primary way to presume group affinity, although neighbor likeness can also be a factor. Link prediction is about inferring future (or unseen) connections based on network structure. Heuristic Link Prediction algorithms are often used to predict behavior. In addition to community detection algorithms, similarity algorithms are also used to understand groupings. » What patterns are significant? Uncovering network patterns reveals similarities and can also be used for general exploration. FIGURE 1-3: GDS questions fall into four different areas. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

8 Graph Data Science (GDS) For Dummies, Neo4j Special Edition For example, you may look for a known relationship pattern between a few nodes or compare attributes of all your nodes to find similarities. Or perhaps you want to evaluate the entire structure of a network, with its intricate hierar- chies, to correlate patterns to certain social behavior to investigate. Aggregating related but ambiguous information in large datasets is a common activity that relies on finding similar and related information. Finding patterns may employ simple queries or various types of algorithms found in Chapter 3. Multiple types of graphs queries and algorithms are usually applied in a recipe fashion as part of a GDS workflow. For exam- ple, a query to understand the density of relationships in a graph may help determine the appropriate community detection algo- rithm for the most relevant results. Tactically, graph queries and algorithms are the tools for understanding the overall nature of a connected system and for using relationships in various data science pipelines. THE RISE OF GRAPH DATA SCIENCE The rise of graph data science (GDS) is the result of more accessible technologies, increased ability to compute over massive graph datas- ets, and an awareness of the power of graphs to infer meaning and improve forecasts. Researchers play an essential role in developing awareness and advocating for the best techniques. As data scientists see the potency of structural information, they’re increasingly incor- porating graphs into their statistics, analytics, and ML practices. In fact, according to the Dimensions Knowledge system for research publications, the use of graph technology in AI research is accelerat- ing. In the last ten years, the number of AI research papers that fea- ture graph technology has increased over 700 percent. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

CHAPTER 2 Using Graph Data Science in the Real World 9 Chapter 2 IN THIS CHAPTER » Seeing how graphs help the healthcare industry » Using graphs in marketing » Putting graphs to work to prevent fraud Using Graph Data Science in the Real World Today’s most pressing data challenges center around con- nections, not just tabulating discrete data. The ability for graph data science (GDS) to uncover and leverage network structure drives a range of use cases from fraud prevention and targeted recommendations to personalized experiences and drug repurposing. We can’t overstate the impact of improved graph techniques such as new algorithms or the efforts of applied network scientists such as within computational biology. We don’t want you to overlook societal projects that use graphs, either. However, we believe that the recent explosion of graphs in the business world represents a shift in accessibility and opportunity to drive a democratization of graphs for everyone. Graph technologies help organizations with many practical use cases across industries and domains. In the past, many busi- nesses began exploring graph technology to create a 360-degree view of their customers or to unify master data, including cus- tomer, product, supplier, and logistics information. They may use this kind of tracking to improve customer experience or to meet compliance regulations of recent privacy acts such as the EU’s These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

10 Graph Data Science (GDS) For Dummies, Neo4j Special Edition General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This same kind of complete view and data lineage in graphs is also now used to understand and track data used in machine learning (ML) for more responsible artificial intelligence (AI) applications. Today, businesses are just as likely to look at using graphs spe- cifically for data science as they recognize the predictive power of relationships, the ability to use network structures to improve their ML, and their own need to innovate. The sections in this chapter highlight a few GDS use cases in areas of accelerating growth and significant commercial interest. Looking at Graphs in Healthcare It’s easy to see how any industry with biological roots would nat- urally comprehend the importance of interconnected systems. You can see this relationship in computational biology as well as healthcare and life sciences in how they view challenges as part of larger processes. Two examples stand out for serving health and commercial interests: more efficient drug discovery and bet- ter patient outcomes. Discovering more efficient drugs Safety, speed, and costs are paramount in making new drug solu- tions accessible. Graphs can help tackle the complexity of inter- twined relationships between diseases, genes, drugs, side effects, and demographics — to name just a few considerations. One impressive knowledge graph in the life sciences industry integrates over 50 years of biomedical data that includes genes, compounds, diseases, and other information such as symptoms and side effects. One of the projects from the graph predicts new uses for drugs by using the graph topology. The graph helps predict new uses for currently approved drugs by evaluating rela- tionships, network structures, and similarities. Drug repurpos- ing significantly reduces costs and time to market compared to developing and testing new drugs — not to mention the benefit of having more real-world information available about side effects and unexpected results when a drug is already in use. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

CHAPTER 2 Using Graph Data Science in the Real World 11 Improving the patient journey Another area of emerging interest is the use of graphs for map- ping, evaluating, and improving patient journeys. When a patient doesn’t feel well, many factors are in play that may have evolved over a period of time. Likewise, treatments are rarely a single event, especially for chronic or serious illnesses. The tree of pos- sible symptoms, visits, test, care givers, treatment plans, out- comes, and then secondary tests and treatments and so on can branch out into an immense number of possible paths. Imagine the patient treatment options that can be mapped with a graph to better see the sequence alternatives and path splits after each and every test result or visit. In fact, researchers and healthcare providers already employ graphs to better understand what influ- ences patient journeys so they can improve individual outcomes as well as create and compare to optimal paths. Recommendations and Personalized Marketing Making relevant product and service recommendations requires correlating product, customer information, historic behavior, inventory, supplier, logistics, and even social sentiment data. Graph-powered recommendations and targeted marketing help companies provide more appropriate services and experiences to a wider range of users. For example, graph community detec- tion algorithms are used to group customers with interactions or similar behavior for more relevant recommendations. Research shows that graph-enhanced ML can predict customer churn, for example, for uses such as targeted prevention or marketing. Graph analytics are also used to help target offers to online users that are anonymous in name and demographics but not in site behavior. Insights from analysis performed offline are typically rolled into decision models used in production for real-time rec- ommendations, which can include recommendations for products that ship faster based on shifting stock levels or instantly incor- porating data from the customer’s current visit. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

12 Graph Data Science (GDS) For Dummies, Neo4j Special Edition Fraud Detection The amount of money lost to fraud each year is growing, despite increased use of AI and ML to detect and prevent it. To uncover more fraud while avoiding costly false positives, organizations look beyond individual data points to the connections and pat- terns that link them. Organizations use the network structure to augment existing ML pipelines as a practical approach to increase the amount of fraud detected and recovered. Graph feature engineering allows businesses to extract predic- tive elements based on graph queries or algorithms and use that information to train ML models. Improving the predictive accu- racy in fraud detection even small percentage points can result in tens of millions of dollars saved in just a few months. GDS enables companies to stay ahead of the ever-shifting patterns of fraud as well as recover more losses. Head to Chapter 5 where we give you a detailed example of detect- ing fraud with GDS. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

CHAPTER 3 Evolving Your Application of GDS Technology 13 Chapter 3 IN THIS CHAPTER » Bringing together diverse information » Using graph analytics to understand your network » Finding, combining, and extracting predictive elements » Simplifying graphs with embedding » Taking a new approach with graph networks Evolving Your Application of GDS Technology Today, graph data science (GDS) is usually applied in busi- ness with one or more major aims in mind: better decisions, increased quality of predictions, and creating new ways to innovate and learn. These goals are increasingly tied to tangible benefits, such as reduced financial loss, faster time to results, increased customer satisfaction, and predictive lift. You may be trying to improve or automate decision-making by people and domain experts that need additional context. Or perhaps your goal is to improve predictive accuracy by using relationships and net- work structure in analytics and machine learning (ML). Graphs provide a unique structure for learning that helps evolve ML techniques through better abstraction and interpretability. These business goals strongly map to how organizations integrate graph technology into their data science practices. Figure 3-1 dia- grams the major phases of a typical GDS journey. We cover each These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.

14 Graph Data Science (GDS) For Dummies, Neo4j Special Edition of these phases in this chapter. The first three phases of the GDS journey are most prevalent in the commercial world today, and the last two are emerging phases on your GDS journey. Your organization can use practical steps to gain immediate value and then layer more sophisticated techniques in a way that con- tinually increases your return on effort. Knowledge Graphs Knowledge graphs are the foundation of GDS and offer a way to streamline workflows, automate responses, and scale intelligent decisions. At a high level, knowledge graphs are interlinked sets of data points and describe real-world entities, facts, or things and their relationship with each other in a human- understandable form. Unlike a simple knowledge base with flat structures and static content, a knowledge graph acquires and integrates adjacent information by using data relationships to derive new knowledge. As the first phase in GDS, knowledge graphs are often imple- mented to bring together diverse information to help domain experts find related content as well as explore the connections in their data. Knowledge graphs can also add context to applications, such as those in artificial intelligence (AI) systems, so they can make better and faster approximating decisions. This approach is used in AI systems, such as chatbots, that use a knowledge graph, for example, to better route a request for a “bat for my hus- band’s birthday.” In this case, the graph grasps that the request isn’t most likely a flying mammal someone is looking for but FIGURE 3-1: The GDS journey. These materials are © 2021 John Wiley & Sons, Inc. Any dissemination, distribution, or unauthorized use is strictly prohibited.