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Memgraph MAGE 1.1 launch summary

You’ve previously used or heard about PageRank, Community detection, and node2vec algorithms? If this is the case, you already know how useful they can be. Now imagine working with them on dynamic data. With Memgraph MAGE 1.1. library, we introduced features on using mentioned algorithms and many more on top of streaming data sources like Kafka, Redpanda, and Pulsar.

Want to know what’s under the hood? Then visit the Github repository and feel free to give it a star! ☆ If you want to try MAGE right away, go to the Quick start and learn how to use it.

The rest of the article aims to summarise all the important things related to the launch of Memgraph MAGE 1.1. so you can read up on the relevant information as quickly as possible. So, here we go 👇

Announcing Memgraph MAGE 1.1 🚀

Announcing Memgraph MAGE 1.1

Memgraph platform offers novelties with each release, bringing the best of the world of graphs and streaming. This time, we have enriched the Memgraph MAGE library with algorithms that work with dynamic/temporal graphs. With version 1.1, we introduced streaming graph analytics experience with algorithms like PageRank, Community detection & node2vec on top of streaming data sources.

Read more about it in the announcement.

Don’t feel like reading? Check out the video our CEO Dominik has prepared 📹

MAGE 1.1 release overview

[Demo] Dynamic graph algorithms in action on a network of tweets and retweets

Dynamic graph algorithms live demo

A couple of days after the launch, we organized a Memgraph MAGE 1.1 live demo. No worries if you missed it live because the recording is available on our YouTube channel.

We showcased the new Memgraph MAGE 1.1 release on a network of tweets and retweets by applying dynamic algorithms such as PageRank, community detection, and node2vec.

Other features such as Rust compatibility and static graph algorithms are also covered.

The demonstration was hosted by CEO Dominik & CTO Buda, founders of Memgraph. As in the case of the Memgraph 2.1 live demo, they were joined by Ivan from the DevRel team.

Timestamps:

  • [0:50] Introducing Memgraph MAGE 1.1
  • [2:21] What algorithms are available in Memgraph MAGE 1.1 library?
  • [3:04] Dynamic graph algorithms
  • [6:10] How do the graph algorithms work on the Memgraph platform?
  • Analyzing a Twitter network with PageRank, node2vec & Community detection
    • [7:24] Dynamic PageRank showcase
    • [23:59] Dynamic Community detection showcase
    • [40:19] Dynamic node2vec showcase
  • [48:36] Example of Rust random walk

Check out the GitHub repo of the Twitter network analysis demo: https://github.com/memgraph/twitter-network-analysis

Articles on the topic of MAGE 1.1 and dynamic graph algorithms

Node embedding & dynamic graph algorithms

Alongside launch announcement, release overview video and live demo, we prepared a couple of articles on the subject of node embedding & dynamic graph algorithms. Here is the list:

Introduction to Node Embedding

Introduction to Node Embedding image

Find out what node embeddings are, how to generate them, and where to use them.


Dynamic Pagerank on Streaming Data

Learn how to use Dynamic PageRank

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


Dynamic PageRank and a Twitter Network

Dynamic PageRank and a Twitter Network image

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


Monitoring a Dynamic Contact Network with Online Community Detection

Monitoring a Dynamic Contact Network with Online Community Detection image

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


LabelRankT – Community Detection in Dynamic Environment

LabelRankT – Community Detection in Dynamic Environment image

Discover how to detect communities in dynamic networks quickly with LabelRankT.


How Node2Vec Works – A Random Walk-Based Node Embedding Method

How Node2Vec Works – A Random Walk-Based Node Embedding Method image

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


Understanding how Dynamic Node2Vec Works on Streaming Data

Understanding how Dynamic Node2Vec Works on Streaming Data image

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


Link prediction with Node2Vec in Physics Collaboration Network

Link prediction with Node2Vec in Physics Collaboration Network image

Read a guide to understanding how link prediction works with node2vec algorithm.


Check out our Memgraph MAGE 1.1 repo

Check out our Memgraph MAGE 1.1 repo To try out Memgraph MAGE, follow the GitHub installation instruction on the link here.

Drop us a pull request here if you feel inspired and want to help us enhance MAGE further.

If you encounter any problems, the documentation is always at your service, or reach out to us on our community forum.

The company of the MAGE

It has been quite an adventure to develop Memgraph MAGE 1.1. Here is the fellowship behind it.

Photo of Memgraph people

And the journey does not end here. We’re always looking for awesome people to join Memgraph. Check our open positions on https://memgraph.com/careers.

Join us on Discord 👋

Our growing community of developers is here to help unlock a whole new world of graph-based applications on top of your streaming data. Engage in meaningful and valuable conversations with other Memgraph developers and the Memgraph team. We are all here with the same goal - building world-class graph applications.

Join us on Discord

Give Memgraph a try

If you’d like to take Memgraph for a spin, you can download it for free.

Visit our Download Hub

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