Neo4j.rb
An active model wrapper for the Neo4j Graph Database for Ruby. (by neo4jrb)
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)
Neo4j.rb | gqlalchemy | |
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1 | 10 | |
1,394 | 207 | |
0.2% | 1.9% | |
6.5 | 7.1 | |
9 days ago | about 2 months ago | |
Ruby | Python | |
MIT License | 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.
Neo4j.rb
Posts with mentions or reviews of Neo4j.rb.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-29.
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[D] Seeking Advice - For graph ML, Neo4j or nah?
The native Python client does add additional overhead when training GNN models. I have also found older reported issues regarding performance hits with neo4j's python driver. Now, these issues may not be strictly pertinent to our current use-case, but they reflect an underlying concern: Performance degradation when interaction between neo4j and native python is considered.
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 Neo4j.rb and gqlalchemy you can also consider the following projects:
Hanami::Model - Ruby persistence framework with entities and repositories
pymgclient - Python Memgraph Client
Sequel - Sequel: The Database Toolkit for Ruby
mgclient - C/C++ Memgraph Client
Guacamole
Memgraph - Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.
MongoModel - Ruby ORM for MongoDB (compatible with Rails 3)
graphtage - A semantic diff utility and library for tree-like files such as JSON, JSON5, XML, HTML, YAML, and CSV.
ROM - Data mapping and persistence toolkit for Ruby
twitch-analytics-demo - Visualization of Twitch analytics.
Ohm - Object-Hash Mapping for Redis
cugraph - cuGraph - RAPIDS Graph Analytics Library