Algorithmic Methods of Data Mining (Sc.M. in Data Science) Academic year 2023–2024. Homewok 5
The Group is composed by the following members:
- Laura Concari (1890490, concari.1890490@studenti.uniroma1.it)
- Arsh Bakhshaee Babaroud (2105709, Arashbakhshaee@gmail.com)
- Francesco Proietti (1873188, proietti.1873188@studenti.uniroma1.it)
- Claudio Giannini (2093898, giannini.2093898@studenti.uniroma1.it)
Main.ipynb: this file contains all the answers to the Research Questions, including AQ.output_commandline.png: this file contains images required to answer the Command Line Question (CLQ)CommandLine.sh: shell script used to answer the first part of the Command Line Question (CLQ)CommandLine_files: This folder contains three files (betweenness_centrality.csv, degree_of_citation.csv, path_lengths.csv). These files were created using the functions in the main.ipynb file at '[4] Command Line Question (CLQ)'
This repository represents the submission of Group 8 for the ADM-HW5 of the Data Science course for the academic year 2023/2024 at Sapienza - University of Rome. The primary purpose of the code is to execute steps 1 and 2 of the homework assignment. The dataset utilized contains citations of scientific articles. Two distinct graphs have been constructed to fulfill the assignment requirements for visualization. The implementation relies on various libraries, including Pandas, Numpy, and SciKit-Learn. These libraries are instrumental in data manipulation and graph analysis of citation and collaboration patterns. Additionally, it is worth noting that a preprocessing step involves the conversion of a JSON (JavaScript Object Notation) file to a CSV (Comma-Separated Values) format.