Lefteris-Souflas / Temporal-Graph-Creation-and-Structure-Exploration

This assignment involves creating a temporal graph structure from Twitter data, exploring metrics over five days, identifying important nodes, and detecting communities. Deliverables include a concise report and source files with code.

From raw data to temporal graph structure exploration

Assignment 2 for the Social Network Analysis Course of AUEB's MSc in Business Analytics.

General Instructions

Your answers should be as concise as possible.
Submitting answers: Prepare a report with your answers on this homework in a single PDF file named p2.pdf.
Submitting code: Prepare the source file(s) with your code.

Problem

1. Twitter mention graph
   Create a weighted directed graph with igraph using raw data from Twitter. Manipulate the raw data to create 5 .csv files, each representing the weighted directed mention graph for the respective day of July 2009. Identify the most important topic for each user based on their hashtags and create 5 .csv files, each containing the user and their most important topic.

2. Average degree over time
   Create plots visualizing the 5-day evolution of different metrics for the graph:

   • Number of vertices
   • Number of edges
   • Diameter of the graph
   • Average in-degree
   • Average out-degree

   Provide observations on the fluctuations of these metrics during the five days.

3. Important nodes
   Write code to create and print data frames for the 5-day evolution of the top-10 Twitter users with regard to:

   • In-degree
   • Out-degree
   • PageRank

   Provide comments on the variations of the top-10 lists for different days.

4. Communities
   Perform community detection on the mention graphs using fast greedy clustering, infomap clustering, and Louvain clustering on the undirected versions of the 5 mention graphs. Write code to detect the evolution of communities for a random user that appears in all 5 graphs. Visualize the graph using a different color for each community, filtering out small or large communities for a meaningful visualization. Include comments on the performance of the clustering algorithms and observations on the communities and their topics of interest.