Lately, I have been on a quest to learn as much as possible about node embedding techniques. The goal of node embedding is to encode nodes so that the similarity in the embedding space approximates similarity in the original network. In layman’s terms, we encode each node to a fixed size vector that preserves the similarity of the original network.
Node embeddings are helpful when you want to capture network information in a fixed-size vector and use it in a downstream Machine Learning workflow.
Andrew constructed a co-occurrence network of book characters. If two characters appear within some distance of text between each other, we can assume that they are somehow related or they interact in the book.
I decided to create a similar project but choose a popular book with no known (at least to me) network extraction. So, the project to extract a network of characters from the Harry Potter and the Philosopher’s Stone book was born.
I did a lot of experiments to decide the best…
This is the third article in my Twitchverse series. The previous two are:
Don’t worry. This article is standalone, so you don’t need to examine the previous ones if you aren’t interested. However, if you are interested in how I constructed the Twitch knowledge graph and performed a network analysis, check them out. You can also follow along by loading the database dump in Neo4j and all the code is available as a Jupyter notebook.
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…
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…
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.
You can follow this link to automatically create a Movies sandbox project. If you choose to, you…
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.
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.
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 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.
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.
Data explorer. Turn everything into a graph.