cugraph VS mage

cuGraph does some of what Networkx does, but it is far from being as easy to use. But it should be fast.

https://en.wikipedia.org/wiki/Sparse_matrix :

> The concept of sparsity is useful in combinatorics and application areas such as network theory and numerical analysis, which typically have a low density of significant data or connections. Large sparse matrices often appear in scientific or engineering applications when solving partial differential equations.

CuGraph has a NetworkX-like API, though only so many of the networkx algorithms are CUDA-optimized.

https://github.com/rapidsai/cugraph :

> cuGraph operates, at the Python layer, on GPU DataFrames, thereby allowing for seamless passing of data between ETL tasks in cuDF and machine learning tasks in cuML. Data scientists familiar with Python will quickly pick up how cuGraph integrates with the Pandas-like API of cuDF. Likewise, users familiar with NetworkX will quickly recognize the NetworkX-like API provided in cuGraph, with the goal to allow existing code to be ported with minimal effort into RAPIDS.

> While the high-level cugraph python API provides an easy-to-use and familiar interface for data scientists that's consistent with other RAPIDS libraries in their workflow, some use cases require access to lower-level graph theory concepts. For these users, we provide an additional Python API called pylibcugraph, intended for applications that require a tighter integration with cuGraph at the Python layer with fewer dependencies. Users familiar with C/C++/CUDA and graph structures can access libcugraph and libcugraph_c for low level integration outside of python.

/? sparse

I feel like you would need to develop a custom solution which might in part store data in Neo4j but you will have to figure out how to efficiently pull the data you need to train your GNNs; and I think this tends to be the bottleneck since Graph DBs are not optimised for the kinds of queries you need for GNNs. For what it's worth, I wouldn't really bother with implementing a custom graph data structure (unless I was really keen) as there are some good implementations out there. Have you looked at cuGraph for example?

You can find the MAGE repository on this link.

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.

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.

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.

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.

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!

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).

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.

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.

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.