This repository contains code for analyzing the Facebook dataset obtained from SNAP (Stanford Network Analysis Project). The dataset represents a social network, where nodes represent Facebook users and edges represent friendships between users.
graph_fb.c: This file contains the code to read the Facebook dataset from a file and create a graph data structure.diffusion_degree.c: Implements the calculation of diffusion degree scores for nodes in the graph.mid.c: Implements the calculation of maximum influence degree scores using the Facebook dataset fromfacebook_combined.txt.modularity.c: Calculates the modularity of a partitioning of the graph.modularity_on_greeding.c: Implements the greedy algorithm for maximizing modularity.partitioning.ipynb: Jupyter notebook containing the analysis of partitioning algorithms. Calculates deficiency and measures time complexity of the partitioning algorithm, finding ( O(n^2 \log n) ) time complexity.partition_with_mid.ipynb: Jupyter notebook continuing the analysis, focusing on time complexity, which is found to be ( O(n \log n) ).
- Compile the C files using an appropriate compiler (e.g., gcc).
- Run the compiled executables to perform various analyses on the Facebook dataset.
- Refer to the Jupyter notebooks for a detailed analysis, including time complexity measurements and comparisons between different algorithms.
- C Compiler (e.g., gcc)
- Jupyter Notebook
- Python 3.x
- Libraries: matplotlib, numpy, scipy
- Ensure the dataset files (
facebook_combined.txt,facebook_cpmbined.txt) are present in the same directory as the code files. - Results, visualizations, and conclusions are provided within the Jupyter notebooks for each analysis.