Massive graph management and analytics

This repository contains folders with laboratory work performed during the course at the University of Paris Saclay.

Table of Contents

• General Info
• Technologies Used
• Contact

General Information

The objectives of this course is to provide the student with knowledge about designing high-performance and scalable algorithms for massive graph analytics. The course focuses on modeling and querying massive graph data in a distributed environment, designing algorithms, complexity analysis and optimization, for massive data graph problem analytics. Upon successful completion of this course, the student is able to:

• model and query massive graph data in a distributed environment
• design and analyse efficient graph algorithms in real-world data-intensive applications;
• develop efficient applications using the best practices in a distributed environment (Spark, MapReduce, Neo4J, GraphX, etc.).

Technologies Used

• Python - version 3.8
• Anaconda - version 2020.11

Contact

Created by @LauraKarimova - feel free to contact me!