Label Propagation Algorithm Implementation and analysis of the results
- Imad Eddine Ibrahim Bekkouch
- Daria Zapekina
- Gcinizwe Dlamini
Big Data Course
Data Science Masters
Innopolis University
Innopolis
Please follow the instructions to get an up and running version of our code running on your local machine.
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
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
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
This project is licensed under the MIT License - see the LICENSE file for details