Automate data
engineering tasks for NetworkX, and make your algorithms faster

Be free of tedious imports and data management, and finally focus on analyzing the data and discovering valuable insights.Write NetworkX code with Python, deploy it directly in a graph database and make results instantly available to your peers.

Speed up development and focus on data analysis

Forget about repetitive, time-wasting loading which slows down the development process so you can focus on running the algorithms and visualizing the results. Let Memgraph handle importing, and updating your graph.

Scale your algorithms to large datasets and get results faster

Eliminate performance bottlenecks in NetworkX by using Memgraph's native implementations of graph algorithms in C++.

Instantly deploy to production and expose your work to apps and peers

Writing endless lines of code to create production-ready applications takes away the time from the work you actually want to be doing - analyzing data.

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Free Course

Learn Python for Graph Analytics!

Learn about the most common graph use cases, the basic concepts of NetworkX and how to make use of graphs with Python to analyse data and gain insights.

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Why use Memgraph with NetworkX?

Persistence and performance

Load the data once, even from multiple and real-time sources and focus on the querying and analysis, not on the tedious and manual importing and data management.

Due to the C++ implementation and highly optimised storage memory usage, Memgraph outperforms Python implemented NetworkX on both small and large scales.

Built-in data visualization library

After querying the dataset directly, shape the graph results so they reflect the insights you've discovered in the best possible way.

Interact with the graph, explore the data, share and present it to others.

A complete platform for data analysis

No coding is necessary to import data, run algorithms and visualize the results. Use a wide range of NetworkX algorithms as well as Memgraph's graph library MAGE which grants you out-of-the-box access to graph algorithms.

You can also easily use the code from your NetworkX project directly inside Python-friendly Memgraph for a smooth transition.

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Data persistency, large scale data analytics and visualizations - explore NetworkX and Memgraph

When NetworkX can no longer handle the analysis and vizualisation requirements of your project, and you are tired of constantly reloading data, find out how you can utilize Memgraph to get your graph data analysis back on track.

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Find other developers performing graph analytics in real time with Memgraph.

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