Memgraph for NetworkX Developers

Finally focus on analyzing data and getting insights rather than on the imports and tedious data management.

Dive into the Graph Analytics with Python course to get started!

Overcome these NetworkX downsides!

Immutable static in-memory data in Python

When a change in the data source or analysis code happens, you need to reload the dataset. Due to the Python implementation, NetworkX graph algorithms become too slow with millions of nodes and relationships.

Slow and hard-to-use visualization tools

NetworkX is not primarily a visualization tool, so the solutions it offers are often too slow and not interactive. Besides, it's time consuming to learn how to use those tools.

Coding the utilities takes time away from analysis

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

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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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