Learn through a practical example how to use graph theory and algorithms to gain valuable insights from connected data

Graph data science focuses on analyzing the connections and relationships in data to gain valuable insights. Every day, massive amounts of data are generated, but the connections between data points are often overlooked in data analysis. With the rise of Graph Data Science tools, the ability to analyze connections is not limited anymore to huge technology companies like Google. In this blog post, I will present how to set up the Neo4j Graph Data Science environment on your computer and walk you through your (first) network analysis. We will be using the Twitch network dataset. In my previous blog post


Learn how to design and construct a knowledge graph in Neo4j that describes the Twitch universe

I was inspired by Insights from Visualizing Public Data on Twitch post. The author uses Gephi to perform graph analysis on the Twitch network and visualize the results. Twitch is an online platform that allows users to share their content via live stream. Twitch streamers broadcast their gameplay or activity by sharing their screen with fans who can hear and watch them live. I wondered what kind of analysis we could make if we used a graph database instead of Gephi to store the network information. This blog post will show you how to design and construct a knowledge graph…


Quickly inspect graph embedding algorithm results in Neo4j graph data science playground application NEuler.

NEuler is a graph data science playground application designed to help you execute and understand graph algorithms in Neo4j. With only a couple of clicks, you can import example data, execute various graph algorithms, and visualize their results. It is available as an extension to Neo4j Desktop, and you can also use it in combination with Neo4j Sandbox.

In this blog post, I will use the Movies sandbox project to demonstrate how to quickly visualize graph embedding results with a t-SNE scatter plot.

Setting Up the Neo4j Sandbox Environment

You can follow this link to automatically create a Movies sandbox project. If you choose to, you…


Implementation of information extraction pipeline that includes coreference resolution, entity linking, and relationship extraction techniques.

I am thrilled to present my latest project I have been working on. If you have been following my posts, you know that I am passionate about combining natural language processing and knowledge graphs. In this blog post, I will present my implementation of an information extraction data pipeline. Later on, I will also explain why I see the combination of NLP and graphs as one of the paths to explainable AI.

Information extraction pipeline

What exactly is an information extraction pipeline? To put it in simple terms, information extraction is the task of extracting structured information from unstructured data such as text.

Steps in my implementation of the IE pipeline. Image by author


How to combine Named Entity Linking with Wikipedia data enrichment to analyze the internet news.

A wealth of information is being produced every day on the internet. Understanding the news and other content-generating websites is becoming increasingly crucial to successfully run a business. It can help you spot opportunities, generate new leads, or provide indicators about the economy.

In this blog post, I want to show you how you can create a news monitoring data pipeline that combines natural language processing (NLP) and knowledge graph technologies.

The data pipeline consists of three parts. In the first part, we scrape articles from an Internet provider of news. Next, we run the articles through an NLP pipeline…


Learn how to use the GraphSAGE embeddings in Neo4j Graph Data Science library to improve your Machine Learning workflows

The use of knowledge graphs and graph analytics pipeline is getting more and more popular. If you keep an eye on the graph analytics field, you already know that graph neural networks are trending. Unfortunately, there aren’t many tutorials out there on how to use them in a practical application. For this reason, I have decided to write this blog post, where you will learn how to train a convolutional graph neural network and integrate it into your machine learning workflow to improve downstream classification model accuracy.

Agenda

In this example, you will reproduce the protein role classification task from the…


Learn how to import, clean, and analyze ArXiv dataset in Neo4j. In the last step, you will learn how to create a search and recommendation engine for articles.

In Europe, we are deep in the second wave of Covid lockdown. I’ve seen some motivational speakers talk about using this time and learning a new skillset. As a child, I’ve always liked nuclear experiments, so I decided to build a reactor in my basement and try some experiments. I’ve already got a basement, so now I only need to learn nuclear physics or maybe get some nuclear researchers to help me out.

I’ve got the idea from Estelle Scifo, who imported and analyzed the ArXiv dataset in Neo4j. We’ll take a detailed look at the nuclear experiments category of…


Hands-on Tutorials, Marvel network analysis

Introducing the new k-nearest neighbors algorithm in the Graph Data Science library

A wise man once said that the 2020–30 decade will be the decade of graph data science. Actually, that happened just a few days ago at the Nodes 2020 conference, and that wise man was Emil Eifrem presenting at the keynote of the Nodes 2020. In case you missed the conference, all the presentations are already available online.

Only fitting Emil’s statement, a pre-release of the 1.4 version of the Neo4j Graph Data Science library was published a couple of days ago. It is a significant milestone for the GDS library. A lot of new features were added in this…


Traveling tourist

A deep dive into pathfinding algorithms available in Neo4j Graph Data Science library

In the first part of the series, we constructed a knowledge graph of monuments located in Spain from WikiData API. Now we’ll put on our graph data science goggles and explore various pathfinding algorithms available in the Neo4j Graph Data Science library. To top it off, we’ll look at a brute force solution for a Santa Claus problem. Now, you might wonder what a Santa Claus problem is. It is a variation of the traveling salesman problem, except we don’t require the solution to end in the same city as it started. …


Traveling tourist

Import data from WikiData and Open Street Map API to create a knowledge graph in Neo4j

After a short summer break, I have prepared a new blog series. In this first part, we will construct a knowledge graph of monuments located in Spain. As you might know, I have lately gained a lot of interest and respect for the wealth of knowledge that is available through the WikiData API. We will continue honing our SPARQL syntax knowledge and fetch the information regarding the monuments located in Spain from the WikiData API. I wasn’t aware of this before, but scraping the RDF data available online and importing it into Neo4j is such a popular topic that Dr…

Tomaz Bratanic

Data explorer. Turn everything into a graph.

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