Python

Are you reluctant to switch from a relational database to a graph databases to explore fraud because you believe you first need to be proficient in Cypher to correctly import the data? Be rest assured - there is a Python-friendly approach available within Memgraph!

If you are spending more time writing code to develop, deploy and manage your graph projects, it’s time you tried Memgraph. It will allow you to focus on the data analysis and free you from all that time-consuming coding.

Are your NetworkX algorithms taking even more and more time to produce the results you need to finish up your research? Or the application reached a critical point and its starting to lag due to increase in data analysis? Could Memgraph tackle the same computations in less time? I think you probably know the answer is “Doh!” but here are the numbers to prove it.

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.

An in-memory database is a database that is kept in the main memory (RAM) of a computer and controlled by an in-memory database management system. When analyzing information in an in-memory database, only the RAM is used.

A Knowledge Graph is a reusable data layer that is used to answer sophisticated queries across multiple data silos. With contextualized data displayed and organized in the form of tables and graphs, they achieve pinnacle connectivity.

Temporal graph neural networks can be used to perform both label classification and link prediction. Learn how to create a simple graph recommendation engine using TGNs on an Amazon product dataset.

Say hello to GQLAlchemy 1.2, a Python OGM (Object Graph Mapper) that helps you work with graph databases.

Through this guide, you will learn how to use different query builder methods to create, change, get, set, and remove data from Memgraph.

Through this short tutorial, you will learn how to import table data from files stored in local or online storage systems to a Memgraph graph database using GQLAlchemy

Through this guide, you will learn how to start, stop, connect to and monitor Memgraph instances with GQLAlchemy.

Through this short tutorial, you will learn how to install Memgraph, connect to it from a Jupyter Notebook and perform data analysis using graph algorithms.

When applying for developer roles, the interviewer might ask you to solve coding problems during technical interviews. This article will help you understand some of the most fundamental ones like BFS, DFS and Dijkstra's algorithm.

Learn how to build applications from bottom to top with the help of GQLAlchemy.

Choose to store python objects partially into an in-memory Graph Database and into an SQLite database on disk.

Learn how to use the GQLAlchemy library to manage data streams and database triggers in Memgraph

A guide to understanding how link prediction works with node2vec algorithm

A step-by-step tutorial on how to identify essential proteins in protein-protein interaction networks using graph analytics

Through this tutorial, you will learn how to create a basic Flask server, Dockerize an app and connect to Memgraph

Learn how to create a real-time recommendation engine with the Online Node2Vec embedding algorithm

Through this tutorial, you will learn how to work with Memgraph from a Python script or a Jupyter Notebook

Use the GQLAlchemy library for easier and more intuitive interactions with Memgraph in Python

In this tutorial, you will learn about the code assignment problem in telecommunication networks and how to solve it in a simple yet effective way using graph algorithms and Memgraph.

Learn the basic principles behind community detection algorithms, their specific implementations, and how you can run them using Python and NetworkX.

Learn how to extend the Cypher query language by implementing a few simple custom procedures to load and export data in a JSON format using Memgraph MAGE and Python.

Learn how to implement custom Cypher procedures using query modules and the NetworkX Python library.

How to build a Python web application for visualizing a Social Network Graph in Python with Docker, Flask and D3.js