https://github.com/mohammadzainabbas/mgma-lab
👨🏻💻 Lab Work for Massive Graph Management and Analytics (MGMA) ✨
https://github.com/mohammadzainabbas/mgma-lab
centralesupelec graph-algorithms graph-analysis graph-manipulation graph-model graph-theory jupyter-notebook python
Last synced: about 1 month ago
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👨🏻💻 Lab Work for Massive Graph Management and Analytics (MGMA) ✨
- Host: GitHub
- URL: https://github.com/mohammadzainabbas/mgma-lab
- Owner: mohammadzainabbas
- License: mit
- Created: 2022-09-23T09:44:20.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-02-03T11:02:08.000Z (over 3 years ago)
- Last Synced: 2025-05-18T19:11:19.540Z (about 1 year ago)
- Topics: centralesupelec, graph-algorithms, graph-analysis, graph-manipulation, graph-model, graph-theory, jupyter-notebook, python
- Language: Jupyter Notebook
- Homepage:
- Size: 7.19 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Lab Work @ Massive Graph Management and Analytics 👨🏻💻
### Table of contents
- [Introduction](#introduction)
- [About the course](#about-course)
* [Main Topics](#main-topics)
- [Labs](#labs)
* [Lab 01 - NetworkX](#lab-1)
* [Lab 02 - Influence Models](#lab-2)
- [Setup](#setup)
* [Create new enviornment](#create-new-env)
* [Setup `pre-commit` hooks](#setup-pre-commit)
#
### 1. Introduction
__`Data drives the world.`__ Nowadays, most of the data (_structured_ or _unstructured_) can be analysed as a graph. Today, many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The scale of these graphs (_in some cases billions of vertices, trillions of edges_) poses challenges to their efficient processing.
#
### 2. About the course
Data we produce or consume has increasingly networked structures which have grown in complexity in different domains such as `biology`, `social networks`, `economy`, `communication` and `transport networks`. The need to process and to analyze such data carries out the emergence of network science research community to define algorithms which allow to characterize such complex structures, to understand their topology, their evolution and to interpret the underlying phenomena.
Besides, the distributed storage and parallel computation technologies offer specific tools for networks based on large-scale graph processing paradigms such as `MapReduce` and `Pregel` of Google.
The purpose of this course is to study the main algorithms and their implementation on artificial and real data in a distributed environment.
#### 2.1. Main Topics
- [x] Preliminaries, Typology of graphs, Graph analytics measures
- [x] Basic algorithms: Random walk and Page Rank
- [x] Label propagation, Community detection, Influence maximisation
- [x] Graph analytics & Deep Learning
#
### 3. Labs
The main aim of this repository is to keep track of the work we have done in __Massive Graph Management and Analytics (MGMA)__ labs. During this course, we will focus on some basic graph algorithms and see how we can utilise these algorithms to efficiently analyse our data. Since, there exist many similarities between graph theory and network science, you will see us using network science related packages as well.
#
#### 3.1. Lab 01 - NetworkX
[NetworkX](https://networkx.org/) is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
Please checkout lab's details [here](https://github.com/mohammadzainabbas/MGMA-Lab/tree/main/src/lab1)
#### 3.2. Lab 02 - Influence Models
Please checkout lab's details [here](https://github.com/mohammadzainabbas/MGMA-Lab/tree/main/src/lab2)
#
### 4. Setup
If you want to follow along with the lab exercises, make sure to clone and `cd` to the relevant lab's directory:
```bash
git clone https://github.com/mohammadzainabbas/MGMA-Lab.git
cd MGMA-Lab/src/
```
> For e.g: if you want to practice lab # 1, then you should do `cd MGMA-Lab/src/lab1`.
#### 4.1. Create new enviornment
Before starting, you may have to create new enviornment for the lab. Kindly, checkout the [documentation](https://github.com/mohammadzainabbas/MGMA-Lab/blob/main/docs/SETUP_ENV.md) for creating an new environment.
#
Once, you have activated your new enviornment, we may have to install all the dependencies for a given lab (kindly check if `requirements.txt` file exists for a given lab before running the below command):
```bash
pip install -r requirements.txt
```
#### 4.2. Setup `pre-commit` hooks
In order to setup `pre-commit` hooks, please refer to the [documentation](https://github.com/mohammadzainabbas/MGMA-Lab/blob/main/docs/SETUP_PRE-COMMIT_HOOKS.md).
#