Graph Algorithms

What is PageRank algorithm? How can it be used in various graph database use cases? How to use it in Memgraph? If these questions are keeping you up at night, here is a blog post that will finally put your mind at ease.

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.

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!

For every problem in the energy management system, there is a graph algorithm that can point you in the right direction! Here is an overview of the most useful graph algorithms for highlighting weak links, high-risk nodes and many more.

How many recommendation algorithms do you need for a successful recommendation engine? Start with these five and we'll recommend you more!

When you notice your traditional IAM system no longer provides adequate analysis and decision making is getting harder as your company grows because you always have to pick up the slack manually, it’s high-time you turn your attention to graphs. They have everything you need - high performance, flexibility and scalability.

If you believe C++ is simply the best, you’ll be happy to hear that the new Memgraph C++ API enables you to effortlessly build query procedures, functions and other analytics tools using your favourite language. What are you waiting for? Code away!

In recent months, users started to ask more frequently about the ability to run algorithms on a subgraph, and we in Memgraph listen to what our users need. Read more in this post How we designed and implemented graph projection feature

Our journey of creating an optimized shortest paths algorithm that returns all paths of same length. Starting from a MAGE query module and working within Memgraph's core, here is what we learned.

Always wanted to identify the most influential developer within your GitHub network? Check out how by using PageRank.

Movie ratings from MovieLens are incoming, but you are still not sure what to watch over the weekend? Create your own movie recommendation system.

Recommending books is hard. Thanks to the Amazon books dataset and insights from graph analytics, you can build your own. Here’s how.

Analyze the Art Blocks transaction data, projects and communities in real-time with Memgraph

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.

Online algorithms are key for rapid response to changes in streamed graph data

Through this short tutorial, you will learn how to install Memgraph, connect to it from a Jupyter Notebook and perform data analysis using graph algorithms.

This article will explore 20 of the most common graph algorithms and various ways to use them in real-life scenarios.

When applying for developer roles, the interviewer might ask you to solve coding problems during technical interviews. This article will help you understand some of the most fundamental ones like BFS, DFS and Dijkstra's algorithm.

The fastest to run any graph algorithm on your data is by using Memgraph and MAGE. It’s super easy. Download Memgraph, import your data, pick one of the most popular graph algorithms, and start crunching the numbers.

Explore a network of retweets using a dynamic PageRank algorithm and a graph database

Explore rumor spreading in a contact network as it changes through time with online community detection

Discover how to detect communities in dynamic networks quickly with LabelRankT

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 the theory behind how to deal with streaming graph data by using Dynamic PageRank

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

A step-by-step tutorial on how to identify essential proteins in protein-protein interaction networks using graph analytics

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

Learn how to get Twitch data, find the most interesting statistics from the Twitch network and visualize the output of different MAGE algorithms on ingested data in real-time