Update README.md

Author: HayaDeeb

README.md

@@ -1,17 +1,66 @@
-# NetworkAnalysis
+# CoAuthorship - Social Network Analysis
 
-Welcome to the Social Network Analysis (SNA) repository! This project provides tools and code to perform social network analysis, generate insightful reports, and visualize interactions within a network. 
+Welcome to the **Social Network Analysis (SNA) repository!**   
+This project focuses on *co-authorship network analysis*, a powerful method for visualising and analysing collaboration patterns among researchers. By representing authors as nodes and their co-authorships as edges, we construct a network that reveals clusters of closely connected researchers, highlights central figures, and identifies critical "cutpoint" authors whose removal would fragment the network.   
 
-## Data format
-Data should be input in .csv format, with one column containing author names.
+This repository provides tools and code to perform social network analysis, generate insightful reports, and visualise interactions within a network.
 
-It is also possible to use a .net file generated by the VOSviewer software.
 
-## Modifying the report
-Run `Generate_report.r` to generate the report in markdown and pdf formats (`Report.md` and `Report.pdf`). Note: generating the pdf requires latex and pandoc to be installed on the system.
+---
+
+## Instructions for Use
+
+### 1. Data format
+The input data should be in `.csv`  format, with one column containing author names. This CSV file can be exported from databases such as PubMed, Scopus, Web of Science, or university research databases. 
+
+### 2. Clone the repository
+Clone the entire repository to your computer using the `git clone` command or the `GitHub Desktop` application.
+
+### 3. Ruuning the repository 
+
+#### 3.1 intsall Packages: 
+Ensure that the following libraries are installed on your computer:   
+(the list of required packages here.)
+
+#### 3.2 Report generating: 
+The primary file you will need is `Generate_report.R` . You can run this R script in RStudio or Visual Studio Code. Each function and line of code is well-documented, with instructions and explanations on how to modify it to suit your analysis needs.
+The code includes various filter functions that you may adjust depending on your research questions, such as:
+
+- The maximum number of authors per paper in your dataset.
+- The minimum number of papers per author to be included in the analysis.
+- The timeframe (years) you wish to study.
+- Whether to split the analysis into smaller time intervals. 
+
+#### 3.3 output folder:
+After running the code, the output (including figures, PDFs, and additional CSV files) will be saved in a folder named `output`. This folder will be created automatically if it does not already exist.
+If you analyse multiple CSV files, a separate subfolder (under the same name of the csv file) will be created within the output folder for each dataset, containing the relevant results.
+
+##### the generated outcomes: 
+      1. `Report_YYYY_YYYY.pdf`  
+      This is the main output of the automated code: a PDF report containing the analysis results. It includes a table of contents, detailed interpretations of the findings, and visualisations with captions. 
+      The corresponding markdown file, `Report_YYYY_YYYY.md`, is also generated.
+
+      2. `centrality_data_YYY-YYYY.csv`
+      This CSV file contains centrality metrics for all authors in the co-authorship network. It includes four columns:
+      - Author: Each row represents an author in the network, along with their centrality metrics.
+      - Degree: The number of direct co-authorship connections an author has in the network.
+      - Closeness: Indicates how quickly an author can reach others in the network, reflecting their proximity to all other authors.
+      - Betweenness: Measures the extent to which an author acts as a bridge between other researchers, highlighting their role in connecting different parts of the network.
+      
+      Use this file to identify influential authors (high degree), those central to information flow (high closeness), and key connectors (high betweenness).
+      
+      3. `figures` Folder 
+      This folder contains three PNG images:
+      - graph.png (Figure 1 in the report): Visualisation of the co-authorship network.
+      - top_authors.png (Figure 2 in the report): Direct connections between the top 15 most central authors.
+      - cutpoints.png (Figure 3 in the report): Highlighted cutpoint authors in the network.
+
+---
+## Repository Contents
+
+
+---
 
-### Changing text
-Report text can be edited in `src/Report_text.r`.
 
 ### Adding new metrics
 To compute new metrics, edit `src/Interactions_class.r` to add a new method (both functions need to be added):