Week 3

Network centrality - case discussion

Prepare

Note📚

📖 Review Week 2 slideshows on network centrality

📖 Revise centrality measures: degree, closeness, betweenness, and eigenvector centrality

Participate

Note👥

🙋 Case Discussion: “Who Is the Right Influencer? A Social Network Analysis”

This week we focus on applying centrality concepts through case study discussion.

Materials:

• Electronic copy of the case available in Moodle’s “case studies” section
• Qualitative discussion of the case (made available after the class)

Practice

Note💻

Hands-on Analysis: Explore the Twitch dataset to understand node attributes before applying centrality measures

• Exploratory data analysis of Twitch gamers

Perform

Note📝

Assignment: Influencer Selection for Space Games Inc.

Apply the strategic framework from the Twitch case discussion to identify optimal influencers using R network analysis.

Materials:

• Assignment brief (perform_1.qmd) - Requirements and submission details

• Worked example: 2×2 Framework Analysis (perform_2.qmd) - Complete R-based implementation of network size vs. diversity framework

• Due: October 20, 2025

• Submit to: Simone.Santoni.1@city.ac.uk

Ponder

Note🤔

Centrality Measures: Commonalities and Differences

Reflect on the four centrality measures discussed (degree, closeness, betweenness, eigenvector):

• Commonalities: What do all centrality measures attempt to capture? How do they help identify important nodes?
• Differences: When would you choose one measure over another? What unique insights does each provide?
• Trade-offs: Can a node rank high on one measure but low on another? What does this tell us?

Consider real-world examples: social media influencers (degree vs. eigenvector), transportation hubs (betweenness), or emergency response networks (closeness).