mage
MAGE - Memgraph Advanced Graph Extensions :crystal_ball: (by memgraph)
gqlalchemy
GQLAlchemy is a library developed with the purpose of assisting in writing and running queries on Memgraph. GQLAlchemy supports high-level connection to Memgraph as well as modular query builder. (by memgraph)
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mage | gqlalchemy | |
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19 | 10 | |
231 | 206 | |
3.5% | 4.9% | |
8.5 | 7.1 | |
3 days ago | about 2 months ago | |
C++ | Python | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mage
Posts with mentions or reviews of mage.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-16.
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How to Implement Custom JSON Utility Procedures With Memgraph MAGE and Python
You can find the MAGE repository on this link.
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Link Prediction With node2vec in Physics Collaboration Network
We will import these 118521 edges, and act as if they are undirected. The Node2Vec algorithm in MAGE accepts parameters whether to treat graph from Memgraph as directed or undirected.
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Optimizing Telco Networks With Graph Coloring & Memgraph MAGE
Memgraph introduces the concept of query modules, collections of user-written Cypher procedures intended to extend Cypher by implementing complex graph algorithms. Various query modules are already implemented and ready to use in an open-source project MAGE.
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Synchronize Data Between Memgraph Graph Database and Elasticsearch
However, there is one more thing that we should really think about when syncing Memgraph and Elasticsearch and that is expandability. The challenge is to build a solution that will need the minimum amount of change when a new method is required. That is why we decided to use Memgraph’s Pythonic capabilities and create a new query module that uses Elasticsearch’s API inside Memgraph’s graph library MAGE.
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How to Identify Essential Proteins Using Betweenness Centrality
Memgraph Advanced Graph Extensions, or for short, MAGE, is an open-source graph library that contains implementations of standard and incremental graph algorithms. You can use any of the algorithms as well as implement your own.
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MAGE Got One More Wizard Called Rust
MAGE is an open-source repository that contains many Memgraph modules in various programming languages. The overall idea is to extend Memgraph with capabilities that are not part of the core engine. One of the critical design decisions early on was the compatibility with various programming languages to support different application stacks and developers with varying skill sets. Up until now, only C/C++ and Python were first-class citizens of the Memgraph ecosystem, but Rust is quickly running through the front doors!
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Production ready Graph Database
Memgraph is a graph database in C++ but heavy on Rust as well. The client -> https://github.com/memgraph/rsmgclient (still early, please report any issues). It's possible to write query modules for Memgraph in Rust -> https://github.com/memgraph/mage/tree/main/rust (the "rusty" wrapper).
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Importing Table Data Into a Graph Database With GQLAlchemy
If you want to do more with your graph data, visit the Memgraph MAGE graph library (and throw us a star ⭐) and take a look at all of the graph algorithms that have been implemented. You can also implement and submit your own algorithms and utility procedures.
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Influencers Among Computer Scientists
Finally, if you are working on your query module and would like to share it with other developers, take a look at the contributing guidelines. We would be more than happy to provide feedback and add the module to the MAGE repository.
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LabelRankT – Community Detection in Dynamic Environment
We at Memgraph recognize your challenges. In this article, you will learn about the merits of online community detection methods and get acquainted with the LabelRankT algorithm by Xie et al., now available in MAGE 1.1.
gqlalchemy
Posts with mentions or reviews of gqlalchemy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-06-16.
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Link Prediction With node2vec in Physics Collaboration Network
As already mentioned, link prediction refers to the task of predicting missing links or links that are likely to occur in the future. In this tutorial, we will make use the of MAGE spell called node2vec. Also, we will use Memgraph to store data, and gqlalchemy to connect from a Python application. The dataset will be similar to the one used in this paper: Graph Embedding Techniques, Applications, and Performance: A Survey.
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Importing Table Data Into a Graph Database With GQLAlchemy
For any other service provider, it is possible to implement your custom importer class, here's how. Don't forget that GQLAlchemy is an open source project, so you can submit your extended functionality on our GitHub repository.
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How to Become a GQLAlchemist?
If you think there is something crucial that is missing or are even willing the try out your expertise in Python and graphs, check out our GitHub repository and feel free to contribute.
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Monitoring a Dynamic Contact Network With Online Community Detection
gqlalchemy – a Python driver and object graph mapper (OGM)
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Neo4j vs Memgraph - How to choose a graph database?
There is a broad number of drivers in many different programming languages available for both solutions. While Memgraph only maintains a few in-house drivers that it develops and supports (C, C++, Python, Rust), most Neo4j drivers can also be used with Memgraph. This is due to the fact that both solutions use the Bolt protocol, labeled property graph model and Cypher query language.
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NetworkX Developers, Say Farewell to the Boilerplate Code
Memgraph natively has several methods of data import - import from files, MySQL or PostgreSQL, and data streams. Memgraph is also highly extendable, and with the help of its Python client, GQLAlchemy, you can import data from almost anywhere.
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Retrieve graph data with Python instead of writing Cypher queries
Source code for GQLAlchemy is available at GitHub repo.
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[D] Seeking Advice - For graph ML, Neo4j or nah?
I think building your graph database/structure can be quite an engineering and time-consuming challenge, as you mentioned, which I would personally avoid. I believe there are some solutions out there that may help you. There is one open source solution for the requirements and concerns you are mentioning. It checks out most of the things you need, functionality, efficiency, and custom low-level optimizations and it is not bulky as the Neo4j Java backend. In essence, we have built Memgraph an in-memory graph database written in C++. The distinctive key feature of DB is that all the data is stored in RAM for fast queries. There is some cool stuff with ML for graphs. Take a look at this blog post about node embedding and recommendation engines, it is native integration with Python and uses PyTorch. There is also the MAGE library for graph algorithms and ML, it is also open-sourced, which is great news for customization and expansions. I share your thoughts on OpenCypher, as being an issue. Memgraph has an object graph mapper (similar to ORM), called GQLAlchemy, and is in Python. There is also a learning curve, but not a different new skill as Cypher. The good thing is allowed various features for graphs manipulation via Python. There are also some other solutions such TigerGraph, Nebula, etc. But I am not very familiar with them. Feel free to explore. I hope this helps! 😁
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Twitch Streaming Graph Analysis - Part 3
Using gqlalchemy we are trying to connect to Memgraph, just like we have done before in our backend.
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Twitch Streaming Graph Analysis - Part 1
As expected, Flask is there, so it will be installed in our container. Next, we have pymgclient, Memgraph database adapter for Python language on top of which gqlalchemy is built. We will connect to the database with gqlalchemy and it will assist us in writing and running queries on Memgraph.
What are some alternatives?
When comparing mage and gqlalchemy you can also consider the following projects:
graph-data-science - Source code for the Neo4j Graph Data Science library of graph algorithms.
pymgclient - Python Memgraph Client
cugraph - cuGraph - RAPIDS Graph Analytics Library
mgclient - C/C++ Memgraph Client
Memgraph - Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.
TerraForge3D - Cross Platform Professional Procedural Terrain Generation & Texturing Tool
graphtage - A semantic diff utility and library for tree-like files such as JSON, JSON5, XML, HTML, YAML, and CSV.
demo-news-recommendation - Exploring News Recommendation With Neo4j GDS
twitch-analytics-demo - Visualization of Twitch analytics.
rsmgclient - Memgraph database adapter for Rust programming language.