Machine Learning

This blog post deals with solving fraud detection problems with graph machine learning. Learn how to load data, training and plot to find out who did it! It’s elementary, my dear reader.

The new version of Memgraph’s open-source graph extension library, MAGE, now supports node classification and link prediction algorithms. Install the new version of MAGE if you would like to write custom algorithms faster by using the C++ API, need the igraph algorithms or k-means clustering.

Graph Neural Networks can be used for a variety of applications but do you know what it takes to create a great recommendation system? Dive deep into the math of GNNs, implement a link prediction module and show everyone how stunning graph machine learning can be!

Industry leaders in the drug discovery and development area use graph technology. Here’s why

In-memory databases primarily rely on RAM storage instead of using hard or external disks for memory storage. Let’s take a look at how in-memory databases work along with some use cases in data science.

Temporal graph neural networks can be used to perform both label classification and link prediction. Learn how to create a simple graph recommendation engine using TGNs on an Amazon product dataset.

A guide to understanding how link prediction works with node2vec algorithm

An in-depth guide to understanding the dynamic node2vec algorithm and its advantages over static algorithms on streaming data

Learn about a graph-based embedding technique for mapping nodes into a low-dimensional space

Find out what node embeddings are, how to generate them and where they can be used

Learn how to create a real-time recommendation engine with the Online Node2Vec embedding algorithm