We designed and implemented graph projection feature
Hi all! I'm one of the developers that were working on this project for some time. In recent months, users started to ask us more frequently about the ability to run algorithms on a part of graph stored in database - subgraph. Now to do this, we extended the implementation of C API we had and brought to you the graph project feature that enables running algorithms on a specific subset of a graph stored in the database. You can now do graph analysis with PageRank, degree centrality, betweenness centrality, or any other algorithm on subgraphs without any additional adjustments. Before you had…
In plain words
Graph projection is a feature that allows users to run graph algorithms on subsets of graphs stored in a database without additional setup. It extends the C API to support analysis on subgraphs using algorithms like PageRank, degree centrality, and betweenness centrality. The tool is designed for developers and data analysts who need to perform graph machine learning tasks, including training and validation on dataset splits within queries, and supports GPU acceleration for algorithm execution.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi all! I'm one of the developers that were working on this project for some time. In recent months, users started to ask us more frequently about the ability to run algorithms on a part of graph stored in database - subgraph. Now to do this, we extended the implementation of C API we had and brought to you the graph project feature that enables running algorithms on a specific subset of a graph stored in the database. You can now do graph analysis with PageRank, degree centrality, betweenness centrality, or any other algorithm on subgraphs without any additional adjustments. Before you had to create new query procedure. You can fire up a graph machine learning algorithm, such as Temporal graph networks and split the dataset inside the query to do training and validation without splitting the dataset programmatically. And last but not least, you can use your graphics card to run one of cuGraph's algorithms on subgraph in terms of seconds. You can find the whole explanation in blog post [1] and you can checkout the code at Memgraph GitHub [2]. Furthermore you can check cuGraph's algorithms we have integrated [3] I'd like to hear your feedback on our approach. Also if you have any general feedback, just write it in the comments :) [1] https://memgraph.com/blog/how-we-designed-and-implemented-gr... [2] https://github.com/memgraph/memgraph/ [3] https://memgraph.com/docs/mage/query-modules/cuda/cugraph
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