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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.

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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.

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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.

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