BigDataGraphClustering

Label Propagation Algorithm Implementation and analysis of the results

Students Participating

Imad Eddine Ibrahim Bekkouch
Daria Zapekina
Gcinizwe Dlamini

Big Data Course

Data Science Masters

Innopolis University

Innopolis

Getting Started

Please follow the instructions to get an up and running version of our code running on your local machine.

Prerequisites

Please make sure you have the following installed.

Python 3.6
Scala 2.12.7
BeautifulSoup 4.6.3
Numpy 1.15+
Pandas 0.24.1

Running the code

First step is to download data and put it in folder

Run the main file with using any of the following arguments:

usage: spark-submit LabelPropagation.jar Main <data>
<data> : string representing the location of the csv file containg the data

Code structure

Quick explanation of the code structure:

.
├── out                         # Output folder for the artifacts
│   └── artifcats               # artifacts
│       └──LabelPropagation_jar # jar file
├── src                         # Code
│   ├── main                    # Source Code
│   │   ├──data                 # Data folder
│   │   ├──python               # python scripts
│   │   └──scala                # Scala code
│   └── test                    # Testing
├── LICENSE                     # MIT LICENCE
├── max.tx                      # Nodes belonging to the biggest cluster
├── min.txt                     # Nodes belonging to the smallest cluster
├── clustering.txt              # cluserting output
├── build.sbt                   # build configuration for project
└── README.md                   # README

License

This project is licensed under the MIT License - see the LICENSE file for details