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 👇
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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 📹
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:
Check out the GitHub repo of the Twitter network analysis demo: https://github.com/memgraph/twitter-network-analysis
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:
Find out what node embeddings are, how to generate them, and where to use them.
Learn the theory behind how to deal with streaming graph data by using Dynamic PageRank.
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.
Learn about a graph-based embedding technique for mapping nodes into a low-dimensional space.
Learn the theory behind how to deal with streaming graph data by using Dynamic PageRank.
Read a guide to understanding how link prediction works with node2vec algorithm.
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.
It has been quite an adventure to develop Memgraph MAGE 1.1. Here is the fellowship behind it.
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.
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.
If you’d like to take Memgraph for a spin, you can download it for free.
