https://github.com/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.
https://github.com/lefteris-souflas/temporal-graph-creation-and-structure-exploration
community-detection edges graph-diameter graph-plot igraph in-degree louvain-algorithm out-degree r vertices
Last synced: 4 months ago
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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.
- Host: GitHub
- URL: https://github.com/lefteris-souflas/temporal-graph-creation-and-structure-exploration
- Owner: Lefteris-Souflas
- License: mit
- Created: 2024-04-18T21:35:22.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-18T21:59:29.000Z (about 1 year ago)
- Last Synced: 2025-01-12T20:33:36.666Z (6 months ago)
- Topics: community-detection, edges, graph-diameter, graph-plot, igraph, in-degree, louvain-algorithm, out-degree, r, vertices
- Language: R
- Homepage:
- Size: 26.2 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.