Well, that’s a wrap! 2021 absolutely flew by, to say the least…
Even in the endless wake of COVID and climate disasters, supply chain issues and political firestorms, the pace just did not relent in the graph database space. So we decided to take a look back at the last 12 months through the lens of some of our top Neo4j blogs.
In honor of 2021, we bring you 21 awesome blogs from three different content ecosystems: our general blog, our blog for developers, and the blogs we’ve created for data scientists. There’s seven of each, so you better jump in and start reading. Enjoy!
Graphs for Artificial Intelligence and Machine Learning
This blog is a transcription of a presentation Jim Webber, Neo4j’s Chief Architect and CFO, did at GraphTour Boston 2019. In his own words (because who can say it better than Jim Webber?) he introduced the subject matter with this: “If there’s any area of computer science that’s prone to nonsense today, it’s artificial intelligence. I’m going to walk you through some no-nonsense definitions of AI-cronyms, share my history with graphs and intelligent applications, and take a little peek into the future of graphs for machine learning and AI.” Cheers to no-nonsense, indeed!
Why DTCP Decided to Invest in Neo4j
It was a huge year for Neo4j. In June, we raised the largest investment round in database history: a $325M series F led by Eurazeo with participation from GV (formerly Google Ventures) and other investors. DTCP is among those funders, and we were delighted when they offered to share the story of their path to graph on our blog.
Certified Neo4j Professional T-Shirts Are Back!
Did you get your free t-shirt yet? When we brought back the apparel prize for anyone who had passed the Neo4j Certified Professional Exam, we were also excited to share the new design, colors, and sizes with our readers. We’re happy to offer a little swag incentive for anyone striving to get certified.
Achieving Liftoff: Reflections on Six Amazing Months in Graph Databases
Lance Walter, Neo4j’s CMO, penned this post looking at the graph database space during the first six months of 2021. We saw lots of exciting growth and implementation – both at Neo4j and the industry at large – as well as an array of explosive trends. (Can you say “Cloud.”) This post is a testament to all the graphy activity that’s already engulfed us, as well as a look at all the amazing opportunities on the horizon.
Knowledge Graphs: Data in Context for Responsive Businesses [New Book]
Our resident knowledge graph guru, Maya Natarajan, wrote this blog announcing a new O’Reilly book, Knowledge Graphs: Data in Context for Responsive Businesses
. This robust announcement unpacks a bit of what is covered in the book and explores the myriad ways knowledge graphs can support businesses. And best of all, she points you directly to where you can download the book – for free!
Please Welcome Patrick Pichette to the Neo4j Board
We at Neo4j were over the moon when Patrick Pichette joined the board. He is a big name with tons of clout and experience – in addition to being a delightful person! – and he and Emil have a truly wonderful camaraderie. (Partly, as they joke, because they’re both Northerners, from Canada and Sweden, respectively). This interview sets the state for a fruitful era of growth and collaboration, and with Patrick on the board, the future only looks brighter.
Neo4j 4.4 – The Fastest Path to Graph Database Productivity, Generally Available
We did it! With a team of engineers, developers, product managers, and an assortment of other nodes working ‘round the clock, we released the latest version of our graph data platform. The 4.4 release is packed with updates and new features, and ensures our graph technology can continue to optimally serve the numerous businesses and industries that benefit from it.
Introducing the New GraphAcademy
This year, we also introduced a new version of Neo4j GraphAcademy, a free, self-paced online training platform. The redesign aims to better align GraphAcademy with our mission to make learning graph database technology effortless, from a new look and feel to shorter length of courses with a clear learning path towards certification.
Behind the Scenes of Creating the World’s Biggest Graph Database
Behold the world’s biggest graph database, demonstrated live at the NODES 2021 opening keynote. Chris Gioran reveals what’s behind the scenes of this massive social network database containing trillion+ nodes and relationships in 1000+ shards, amounting to 280 TB of data.
Digging Into the Pandora Papers Dataset with Neo4j
ICIJ’s Pandora Papers, the most gigantic piece of investigative work this year, reveals the financial secrets of many global power players with the help of Neo4j’s graph database. Following the first data release of the investigation, Michael Hunger broke down the full offshoreleaks database
in Neo4j to show how graph databases were able to help ICIJ ingest, break down, and analyze the data they uncovered and report on their findings.
Turn a Harry Potter Book into a Knowledge Graph
Applying graphs to books and movies has always been beloved, partly because it makes graphs relatable. Tomaz Bratanic created this cool knowledge graph of the book Harry Potter and the Philosopher’s Stone
using Selenium and SpaCy’s pattern matching algorithms that breaks down characters and how they’re related to each other.
Put Data Modeling on the Table
Data modeling is out of date. For decades, relational data models have been the standard. But what if normalized relational schema models can’t represent logical entities efficiently? Our community member Shani Cohen breaks down the limits of the relational data model and discusses what the graph data model can do.
Diversify Your Stock Portfolio with Graph Analytics
There’s a saying, “Don’t put all your eggs in one basket.” Tomaz Bratanic shows you how you can potentially reduce your risk and increase your profit by diversifying your stock portfolio with research backed by graph analytics and algorithms.
15 Tools for Visualizing Your Neo4j Graph Database
A few years ago, it took significant work to create intuitive graph visualizations. Today, a ton of tools have been developed that make graph viz a cakewalk. Niels de Jong shares 15 of his favorite graph tools, categorized by their functionalities – such as development, exploration, analysis, and reporting.
Data Science Blog
Importing CSV Files into Neo4j
The easiest format for Neo4j to ingest data is from a CSV. Here’s your one-stop solution to importing CSV files into Neo4j, whether you’re looking for a simple approach for small graphs or a fast approach for when the graphs become large.
From Text to Knowledge: The Information Extraction Pipeline
The combination of natural language processing and knowledge graphs is one of the paths to explainable AI. Tomaz Bratanic shows you how to create a knowledge graph out of a Wikipedia page with an information extraction pipeline that includes coreference resolution, entity linking, and relationship extraction techniques. Read More
Create a Graph Database in Neo4j Using Python
For new users who aren’t yet familiar with the Cypher query language, Clair Sullivan shows you how you can use your own data generated with Python to populate the Neo4j graph database via the Neo4j Python driver. Read More
Neo4j & DGL – A Seamless Integration
Sometimes data scientists need to deploy external libraries into an existing workflow. Kristof Neys illustrates how you can integrate a Graph Attention Network model using the Deep Graph Library in conjunction with the Neo4j Python driver into the Neo4j workflow. Read More
Getting Started with Graph Embeddings in Neo4j
The starting point for all machine learning is to turn your data into vectors/embeddings. Clair Sullivan gives you a brief introduction to how you can turn the nodes of a graph into vectors using three methods. Read More
Construct a Biomedical Knowledge Graph with NLP
The biomedical field is a prime example where representing the data as a graph makes sense. Here’s how you can combine OCR, named entity linking, relation extraction, and external enrichment databases to construct a biomedical knowledge graph. Read More
A Deep Dive into Neo4j Link Prediction Pipeline and FastRP Embedding Algorithm
The Link Prediction pipeline combines node properties to generate input features of the Link Prediction model. Here’s how to train and optimize Link Prediction models in Neo4j Graph Data Science to get the best results. Read More
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