Join us for the Memgraph MAGE 1.1 live demo

ONLINE EVENT

December 20th
5 PM CET

(press "Reminder On" on Youtube)

Attend the MAGE 1.1 live demo and learn more about stream processing with graph algorithms. Your hosts will be the co-founders of Memgraph, Dominik and Marko. Joining them will be Ivan from the DevRel team.

MAGE is an outstanding extension to Memgraph’s core capabilities. Fast computations powered by Memgraph are now enhanced with advanced dynamic graph algorithms. With version 1.1, we introduce streaming graph analytics experience with algorithms like PageRank, Community Detection & Node2Vec on top of streaming data sources like Kafka, Redpanda, and Pulsar.

Dominik, CEO of Memgraph, recorded a short, less than 5-minutes long video about what's new in Memgraph MAGE 1.1.
Check it out 👉
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Event Agenda

1. What is MAGE?

Learn more how we enriched Memgraph extensions with algorithms that work with dynamic/temporal graphs.

2. What's new in the MAGE 1.2 release?

Find out more about some of the most popular algorithms like PageRank, node2vec & community detection and how to use them to extract knowledge from streaming graph data.

3. Showcasing a MAGE demo application for analysing Twitter networks

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

We will use the new dynamic algorithms to analyze a network of tweets and retweets with the hashtag #christmas.

Is there a better use for the PageRank algorithm than to find the most Christmassy person on Twitter?🎄😁

4. Other features in the new release

Query modules in Rust.
Rust is back for us! After dropping out of version 1.0 due to changes in the API, this time, Rust is coming back stronger than ever. The API is this time compliant with Memgraph version 2.0+. But that’s not all, in addition, support for temporal types has been implemented!
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Static node2vec algorithm.
Last but not least is the most popular graph preprocessing tool. It is one of the most used methods of node embeddings. It serves as an entrance point for numerous machine learning tasks.

LIVE DEMO