Memgraph MAGE

The graph wizard is here, don't forget to bring your magic dust!

[Memgraph MAGE @memgraphmage]

Look, it's me - MAGE 🪄

[Analytics France @AnalyticsFr]

Running Large-Scale Graph Analytics with Memgraph and NVIDIA cuGraph Algorithms - https://t.co/5E63nbM3lh - thanks @RichardEudes #Analytics

[Memgraph @memgraphdb]

Mage 1.2 is here! This release brings the full package of useful graph analytic tools in the terms of dynamic and static graph algorithms, handy utilities, and finally - an introduction to the field of graph machine learning.
https://t.co/YaLoal5FrA

[Memgraph MAGE @memgraphmage]

Agraphcadabra! 🧙

Prepare your magic wands because MAGE 1.1 is out! Explore the land of DYNAMIC graph algorithms!

The only thing that @dtomicevic is missing in this video is a big, gorgeous wizard hat 🎩. We need to get him one.

https://t.co/KJ7Pc0m4xI

#DataScience #Graphs

[Memgraph MAGE @memgraphmage]

When dynamic networks and #MachineLearning meet each other 💥 - MAGIC

@MemgraphDB

[Emanuele Rossi @emaros96]

Check out this new blog post by @mmbronstein and myself to find out what dynamic graphs are, why they are important, and how we can learn on them using Temporal Graph Networks (TGN)! https://t.co/zevVChh4Pf

[Memgraph MAGE @memgraphmage]

Agraphcadabra! 🧙

Some advantages of #DynamicGraphAlgorithms:

1. Changes are local - it is not necessary to execute the algorithm from scratch but only on newly added vertex/edge neighborhood

2. Update is faster than a full re-run of a static algorithm

#ComputerScience

[Memgraph MAGE @memgraphmage]

Exponential growth leads to exponential time complexity. If it takes a millisecond to find one cut-set, it would take us -> 35 years to find all the cut-sets on a fully connected 40-node graph 🤓🪄.

#DataScience
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[Memgraph MAGE @memgraphmage]

Agraphcadabra! 🧙‍♀️

Today’s #FunFactFriday isn’t that much fun for someone who solves exponential problems. One of them is the enumeration of all cut-sets on the graph ...

#ComputerScience
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[Memgraph MAGE @memgraphmage]

A cut-set is a set of edges that, when removed, divide a graph component into two. Printing all cut-sets on a graph is an NP-HARD problem that is that depending on the number of nodes and edges. With more nodes and edges, cutsets grow exponentially. 💥

#ComputerScience
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[Memgraph @memgraphdb]

We are proud to announce that Memgraph 2.1 is finally here! 🎉

Along with Kafka, you can now connect to @apache_pulsar and Redpanda by @VectorizedIO.
There are even more fixes & improvements, so check out the release blog post 👇
https://t.co/6SE2oTNkvx

[Memgraph MAGE @memgraphmage]

Agraphcadabra! 🧙

#FunFactFriday with the big news today. @Google has introduced @TensorFlow Graph Neural Networks - https://t.co/lRuuNDw7dD.

Graphs are getting stronger every day! I'll make sure that we use this magic properly! 💥

#DataScience
#GNN

[Memgraph MAGE @memgraphmage]

How lovely 🧙!
@supe_katarina has used my magic dust in her magnificent "Twitch Streaming Graph Analysis" blog post. Seems like she has been listening to what I've been talking about lately and applied #PageRank to find the most influential streamers.
https://t.co/HlAWSnyIlb

[Memgraph MAGE @memgraphmage]

Agraphkadabra!
I must say thank you to those who helped me build a recommendation system with the new spell - dynamic node2vec. In my opinion, it works fantastic! 💓https://t.co/3XIO89N4Qt
Have you tried my new spell already? I am sure my brother @neo4j already did!

[Memgraph MAGE @memgraphmage]

I think I turned out to be extremely photogenic in this article! 📸

My dear padawan @AntonioFilipo18 wrote an amazing article about #OnlineNode2Vec 🧙. Use your magic wisely!

https://t.co/3XIO89N4Qt

[Memgraph MAGE @memgraphmage]

Once big and global research is conducted, it is simply hard to reference knowledge and papers which are legitimate. If domain knowledge graph is present ➡️ #PageRank values can be used to determine whether the entity is relevant in research or not! 🤓
#DataScience
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[Memgraph MAGE @memgraphmage]

Now for the best part - #PageRank applications.
@Google's search engine is an obvious example. But what about others❓
If you know about others, use this tool from my garage to tackle them!
https://t.co/MoZLkQzD0B
@MemgraphDB
#DataScience
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[Memgraph MAGE @memgraphmage]

Of course, I'll mention some #PageRank applications.
Traffic can be one of them 🚗. Represent streets as nodes, and intersections as edges➡️run #PageRank and find out the most important streets➡️these will be bottlenecks of your traffic system!
#DataScience #Traffic
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[Memgraph MAGE @memgraphmage]

With this concept, we can introduce a dynamic environment! Once a new vertex or edge appears in the network, we can simply update the stored #RandomWalks.

From them, we calculate approximation #PageRank values and that's it!

Beware. Dynamic algorithms are coming to MAGE
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[Memgraph MAGE @memgraphmage]

Agraphcadabra! 🧙‍♂️
Time is money. One way to be faster when calculating #PageRank is by introducing approximations.

Imagine a random surfer on a network and let him randomly pick the next destination. Repeat, and store it as a #RandomWalk 🕸️.

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[Memgraph MAGE @memgraphmage]

Now, for each node calculate the proportion of its appearances in #RandomWalks. This number is the approximation #PageRank.

Read more in research by @Stanford & @Twitter: https://t.co/xh0ADRfAzF

#GraphDataScience
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[Memgraph MAGE @memgraphmage]

The other problem which can make your network influence vanish is called 'rank sink' 🚰. This one occurs once there are no outgoing edges to propagate the importance.

Fortunately, a solution is the same ➡️ introduce teleportation - propagation can teleport to other nodes!

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[Memgraph MAGE @memgraphmage]

Now that we talked about #PageRank and influence flows, we can dive into some technical details. 🔖

Imagine influence score is propagated throughout the network via outgoing edges till a stable state is created - #NodeRanking. However, problems can occur 👾
#GraphLearning
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[Memgraph MAGE @memgraphmage]

The first problem is called 'spider traps' 🕸️. It occurs once there is a closed cycle in the #graph. In a closed cycle, the nodes successively increase each other's influence scores, absorbing the influence flow!

Learn more on: https://t.co/MoZLkQzD0B

#MemgraphDB
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