September 2022 Summaries

This book provides an expert deep dive into building full stack GraphQL applications. It covers the technologies used throughout the book, including GraphQL, React, Apollo, and Neo4j Database. The book introduces the concept of thinking in graphs and explores how to build a simple business review application using these technologies. It assumes some basic knowledge of web development, JavaScript, Node.js, APIs, and databases, but does not require experience with each specific technology. The goal is to show how these technologies fit together and provide a full stack framework for building applications with GraphQL. The book covers GraphQL type definitions, querying with GraphQL, and how to model application data as a graph using SDL. It provides examples and explanations of how to traverse the data graph through GraphQL operations and execute queries against a GraphQL schema defined by these type definitions.

We are excited to announce our opening keynote speaker this year: Nicholas A. Christakis, MD, PhD, MPH, a social scientist and physician who conducts research in biosocial science, network science, and behavior genetics, will discuss the effects of social networks on population behavior and how they can be used to create an artificial tipping point.``The NODES key speakers have knowledge to share about graph technology, including deploying modern applications, creating innovative knowledge graphs, and data manipulations, with topics such as Spring Cloud Function, Graph Machine Learning, and Neo4j technologies.``A training series will be hosted in October and November, covering a wide range of topics related to Neo4j, including intro to Neo4j, healthcare analytics using Neo4j, handling Neo4j data with Apache Hop, and graph EDA using the Neo4j GDS Client.

Neo4j is a knowledge management system that allows Rob Orlando to create a mastermind and build it into a graph, enabling him to combine near-real-time data with expert knowledge to derive data-driven solutions. He uses Neo4j AuraDB to identify the fastest and cheapest options for his clients, making better decisions faster and accelerating the decision-making process. The Bloom visualization tool is extremely useful in presenting customer presentations, and Rob has found it easier to support multiple clients once he learned how to use graphs. To get started with AuraDB Free, Rob advises users to start with an objective, understand the steps required to meet that objective, and focus on only the datasets needed for that use case.

This week we’re featuring a post by biomedical engineer Tom Nijhof, who explains how his performance issues were solved by using Cypher query parameters. This allows for the query plan to be cached and reused. The biggest graph technology event of the year, NODES 2022, is back November 16 and 17, 2022! We are honored to welcome as speakers: Ward Cunningham, the creator of Wiki; Ashleigh Faith, host of IsA DataThing YouTube channel; Rhys Evans from The Financial Times; and Kateryna Nesvit, Visiting Professor, Data Science at Marymount University. Dr. Kateryna Nesvit has a Ph.D. in Mathematical Simulation and Methods of Calculation and master’s degrees in applied mathematics. She has been teaching in at a university level for 11 years with courses in applied and computational mathematics such as numerical analysis, data science, knowledge graph, data analysis, recommendation systems, and others. Dr. Nesvit has published 65 papers, including one scientific patent.

The Neo4j Graph Platform is a powerful tool for data science, offering several benefits such as easy-to-use low-code and no-code tools, integrations, workflows, and quick onboarding via cloud. The platform includes the Neo4j Graph Data Science library, which provides algorithms and machine learning capabilities to answer business critical questions and improve predictions. The CORA dataset is used to demonstrate the effectiveness of Neo4j Graph Data Science in predicting paper categories with high accuracy. The Graph Data Science library enables users to build knowledge graphs, detect outliers, clusters, and trends, and predict the future by feeding machine learning models with graph features. The platform also supports various connectors, including Apache Spark and Apache Kafka, and provides developer tools such as Neo4j Desktop and Neo4j Browser. By leveraging the Neo4j Graph Data Science library, users can gain several points of accuracy in predicting paper categories and improve their overall data science capabilities.

This week, Trinette Brownhill, a Distinguished Engineer at IBM, shared her experience using and advocating for Neo4j, a graph database technology. She was among the hundred attendees at GraphConnect this year and found time to chat with us about her journey with Neo4j. Trinette's background in data and her consulting work across industries made her perspective valuable, and she highlighted the ease of use and visualization capabilities of Neo4j as key benefits. As a result, she has seen surprising results from using Neo4j, including quick data analysis and demonstration capabilities. Trinette advises newcomers to explore Neo4j's resources, such as articles, blogs, cheat sheets, and toolkits, to get started with the technology. Looking ahead, Trinette believes that graphs will play a crucial role in representing connectedness and decentralization, and she thinks we need to figure out how to demonstrate this connectedness effectively.

The Neo4j community has been actively sharing their projects and knowledge with each other. Florent Biville presented a tech talk on the Neo4j extension for Liquibase, showcasing its capabilities in a schemaless world. Meanwhile, Luke Cassady-Dorion built a Node.js project to graph NFT data using Neo4j, demonstrating interesting patterns of NFT collections. Additionally, Suliman Sharif shared his experience with porting Global-Chem graph infrastructure into Neo4j using a connector, and Sul tweeted about the benefits of using Neo4j Bloom feature in chemistry research. The community is also exploring new tools and features, such as the ICS Packet Capture Visualizer README update on Github, which now serves as a mini-lab for iPthyon, PyShark, and Neo4j users.

Bonsai Data Solutions and Cambridge Intelligence have partnered to help clients understand their customer journeys. The traditional top-down approach measures business outcomes, while the bottom-up approach uses digital analytics to track individual customer interactions. However, both approaches share challenges such as joining data, missing critical dimensions, and inferring causality. A graphy approach, on the other hand, can provide a more detailed understanding of customer behavior by visualizing individual data elements and their relationships. Bonsai's tool, Pyxis, is designed to help business intelligence analysts understand customer journeys in detail, with features such as grouping nodes, path highlighting, and performance visualization. By using graph data platforms like Neo4j Bloom, businesses can measure traditional media measurement, promotions and media, and long-term impact analysis more efficiently and effectively.

The book "Full Stack GraphQL Applications" by William Lyon is now available in print and ebook formats from Manning Publications, showcasing how to develop full stack GraphQL applications using various technologies such as GraphQL, React, Apollo, Neo4j database, and cloud services like Neo4j Aura and AWS Lambda. The author demonstrates how to build a GraphQL API without writing resolvers using the Neo4j GraphQL library, add custom logic with Cypher, and protect data with authorization rules. Additionally, the book is accompanied by a free digital copy download link and an opportunity to participate in the Neo4j Code Golf challenge for a chance to win prizes totaling $27,000. The author Charchit Kapoor is also featured, sharing his expertise on full-stack development and helping users solve technical issues. Other topics include graph visualization, connecting chemical compounds to each other using GraphXR, and translating English phrases into Cypher queries.

Thomas Larsen, a Senior Manager of Forensic Analytics at ABB, has successfully integrated Neo4j into his work with forensics analytics, discovering connections that would have been difficult to find with traditional databases. He found the power of graph technology in applying it to a simple pilot and was able to integrate it into his existing infrastructure, making it easy for his team to adopt. Larsen advises others to start with a simple pilot project to test the tool and its potential benefits. He is excited about the future of graphs and sees possibilities in areas such as document flows, SAP, and graph data science, which could help him and his team be more intelligent about capturing emerging integrity issues.

Neo4j Graph Data Science enables organizations to analyze and optimize their supply chain networks by applying pathfinding algorithms to identify new and faster shipment routes, facilitating downstream optimization problems for freight forwarding recommendations, and conducting what-if scenario simulations to understand alternative routing and conduct risk assessment. The analysis leverages the Cargo 2000 Case Study dataset to demonstrate the application of graph data science techniques in supply chain management. By using Neo4j's Graph Data Science implementation of Yen's pathfinding algorithm, organizations can solve complex optimization problems efficiently and gain insights into their supply chain performance, such as identifying alternative routes and assessing risks associated with high centrality airports.

UX design is centered around user needs, aiming to simplify complex systems and provide a better experience for end-users. Poor UX can lead to a "data monster," causing users to struggle with the application and leading to frustration and abandonment. Design thinking is a process that involves empathy, defining problems, ideation, prototyping, and testing to create a solution that meets user needs. By applying design thinking principles, data-heavy applications can reduce cognitive strain and increase user trust. The process can be applied early in the discovery phase, ensuring everyone involved is speaking the same language and working towards a common goal.

Tom from Graphileon demonstrates the latest version of their CSV import wizard for Neo4j, allowing users to create nodes and relationships with properties. Ben Simpson shares his experience integrating Neo4j into a Rails application in an organized way, modeling a medical professional network as a knowledge graph. Sixing creates a graph solution for companion planting by creating a knowledge graph from available data to gain insights. A new feature is announced for the Neo4j Data Importer, allowing users to preview their high-fidelity loads before importing them into Neo4j. Additionally, there are mentions of integrating DGL and Neo4j DBMS for social analysis, using GraphXR to visualize issues in Atlassian Jira and Airtable, and a tweet discussing benefits of graph databases like Neo4j.

We've made significant enhancements in Neo4j AuraDB to improve user experience. The unified Workspace brings together powerful graph tools, simplifying workflows and data loading. New sample datasets are available, increasing the size limit for free tier users, allowing them to explore and create graph databases. Additionally, instance password downloads provide secure access, and Data importer previews enable quick iteration. Fine-tuning current instances is also possible, with enhanced observability and scalability options available in AuraDB Enterprise.