Network Analytics (SMM638)

This is the official repository for SMM638 - Network Analytics, an MSc module at City, University of London. The course teaches network analysis theory and practice using R and Python, with a focus on business and organizational applications.

🌐 Course Website: https://simonesantoni.github.io/net-analysis-smm638

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📚 About This Course

Networks are ubiquitous in modern life:

• Job opportunities reach us through interpersonal connections
• Social media content is shaped by our digital relationships
• Market valuations emerge from social influence spreading across networks
• Innovation diffuses through organizational and professional networks

This module provides students with cutting-edge network theories and practical analytical tools to understand the organization and functioning of diverse networks. The goal is to develop a distinctive perspective on how networks create value for individuals, organizations, and communities.

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🎯 Learning Objectives

By the end of this module, students will be able to:

1. Understand Network Properties

   • Analyze structural characteristics of networks (density, centralization, clustering)
   • Identify network substructures (dyads, triads, motifs)
   • Evaluate reciprocity and transitivity patterns

2. Analyze Communities

   • Detect community structures using multiple algorithms
   • Evaluate community quality and overlap
   • Understand modularity and community evolution

3. Assess Individual Positions

   • Calculate node centrality measures (degree, betweenness, closeness, eigenvector)
   • Identify influential nodes and structural holes
   • Analyze brokerage positions

4. Visualize Networks

   • Create effective network visualizations
   • Choose appropriate layouts for different network types
   • Communicate network insights visually

5. Apply Technical Skills

   • Use R packages: igraph, tidygraph, ggraph, network, sna
   • Use Python libraries: networkx, graph-tool, plotly, pyvis
   • Implement statistical tests for network properties

6. Solve Real-World Problems

   • Apply network analytics to business challenges
   • Design recommendation systems
   • Analyze organizational structures
   • Study information diffusion

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📁 Repository Structure

net-analysis-smm638/
├── website/                    # Quarto website source (PRIMARY)
│   ├── weeks/                 # Weekly course materials
│   │   ├── week-1/           # Introduction to networks
│   │   ├── week-2/           # Network visualization
│   │   ├── week-3/           # Node centrality
│   │   ├── week-4/           # Dyads and triads
│   │   ├── week-5/           # Network dynamics & case study
│   │   ├── week-6/           # Community detection
│   │   └── week-7-10/        # Advanced topics
│   ├── course/               # Course information
│   │   ├── syllabus.qmd      # Course syllabus
│   │   ├── schedule.qmd      # Weekly schedule
│   │   ├── support.qmd       # Office hours & help
│   │   └── team.qmd          # Teaching team
│   ├── project/              # Course projects
│   │   ├── midTermProject/   # Mid-term project specs
│   │   └── finalCourseProject/ # Final project specs
│   ├── _quarto.yml           # Website configuration
│   ├── theme.scss            # Light theme
│   ├── theme-dark.scss       # Dark theme
│   └── _site/                # Generated website (auto-built)
│
├── data/                      # Network datasets
│   ├── soundcloud/           # SoundCloud case study data
│   ├── deezer/               # Music streaming network
│   ├── twitch/               # Gaming platform network
│   └── xoxoday/              # Employee network
│
├── tutorials/                 # Python tutorials
├── finalCoursework/          # Final coursework materials
├── CLAUDE.md                 # Repository guide for AI assistants
└── README.md                 # This file

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🆕 Recent Updates

Week 5 (Latest)

• 📊 SoundCloud Case Study: Platform ecosystem analysis with network dynamics
• 📈 Network Properties Analysis: Comprehensive reciprocity and transitivity testing
  • Implemented Conditional Uniform Graph (CUG) tests
  • Statistical comparison with Erdős-Rényi random graphs
  • Fixed package conflicts between sna and igraph
• 🔧 Technical Improvements:
  • Added freeze: auto for reproducible builds
  • Resolved GitHub Actions deployment issues
  • Enhanced data visualization with course branding

Week 4

• Dyads and Triads: Comprehensive analysis of network substructures
• Structural Balance: Theory and practice of signed networks
• Practical Exercises: Hands-on dyad census and triad census calculations

Week 3

• Node Centrality: In-depth coverage of centrality measures
• Interactive Visualizations: Custom network plots with ggraph
• Practice Exercises: Centrality calculations on real networks

Week 1-2

• Network Terminology Glossary: Interactive visualizations covering:
  • One-mode and two-mode networks
  • Directed vs undirected networks
  • Signed and weighted networks
• Enhanced Materials: Updated with visual examples and case studies

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🚀 Getting Started

Prerequisites

For R Users

# Install required R packages
install.packages(c(
  "tidyverse",      # Data manipulation and visualization
  "igraph",         # Network analysis
  "tidygraph",      # Tidy network manipulation
  "ggraph",         # Network visualization
  "network",        # Network objects
  "sna",            # Social network analysis
  "ergm",           # Exponential random graph models
  "intergraph"      # Convert between network formats
))

For Python Users

# Create conda environment
conda env create -f smm638.yaml
conda activate smm638

# Or install with pip
pip install numpy scipy matplotlib pandas networkx plotly pyvis

Building the Website Locally

# Clone the repository
git clone https://github.com/simonesantoni/net-analysis-smm638.git
cd net-analysis-smm638

# Navigate to website directory
cd website

# Render the entire website
quarto render

# Or preview with live reload
quarto preview

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📖 Course Content

Weekly Structure

Each week follows a consistent 5P framework:

1. 📚 Prepare: Pre-class readings and materials
2. 👥 Participate: Interactive lectures and discussions
3. 💻 Practice: Hands-on coding exercises
4. 📝 Perform: Assessed assignments
5. 🤔 Ponder: Reflection questions and further reading

Core Topics

Week	Topic	Key Concepts
1	Introduction to Networks	Network terminology, data structures, basic visualization
2	Network Visualization	Layout algorithms, aesthetic principles, interactive plots
3	Node Centrality	Degree, betweenness, closeness, eigenvector centrality
4	Dyads & Triads	Subgraph analysis, structural balance, transitivity
5	Network Dynamics	Reciprocity, platform ecosystems, case study
6	Community Detection	Modularity, Louvain, Label Propagation algorithms
7	Two-Mode Networks	Bipartite graphs, affiliation networks, projections
8	Exponential Random Graph Models	Statistical modeling of networks, ERGM estimation
9-10	Advanced Topics	Temporal networks, diffusion, link prediction

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🔬 Featured Analyses

SoundCloud Network Analysis (Week 5)

Comprehensive analysis of a music streaming platform with:

• 8,000 users, 1.2M tracks, 100K+ social connections
• Exploratory Data Analysis: User behavior, content patterns, engagement metrics
• Network Properties Testing: Statistical tests for reciprocity and transitivity
• Visualization: Interactive network plots and statistical distributions

Technologies: R, igraph, tidyverse, sna, CUG tests

Key Features

• Rigorous Statistical Testing: Both Erdős-Rényi and Conditional Uniform Graph (CUG) tests
• Reproducible Research: Frozen computational outputs for CI/CD compatibility
• Professional Visualizations: Course-branded plots with consistent styling

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🛠️ Technical Stack

Website Infrastructure

• Framework: Quarto - Scientific and technical publishing system
• Languages: R (primary), Python (supplemental)
• Deployment: GitHub Pages via GitHub Actions
• Theme: Custom dual-mode (light/dark) with Atkinson Hyperlegible font

R Packages

# Core network analysis
igraph, tidygraph, ggraph, network, sna

# Statistical modeling
ergm, btergm, statnet

# Visualization
networkD3, visNetwork, ggplot2, gridExtra

# Data manipulation
tidyverse (dplyr, tidyr, purrr, readr)

Python Libraries

# Core network analysis
networkx, graph-tool

# Visualization
plotly, bokeh, pyvis, matplotlib, seaborn

# Data manipulation
numpy, scipy, pandas

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📊 Datasets

The repository includes several curated network datasets:

Dataset	Nodes	Edges	Type	Description
SoundCloud	8,000	100K+	Directed	Music platform with users, tracks, follows
Deezer	50K	200K+	Undirected	Music streaming social network
Twitch	10K	30K+	Directed	Gaming platform follower network
Xoxoday	500	1.5K	Undirected	Employee collaboration network

Data Location: data/ directory (some files are .gitignored due to size)

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🎓 Assessments

Mid-Term Project (30%)

• Network analysis of real-world dataset
• Individual work
• Due: Week 6

Final Project (70%)

• Comprehensive network analytics project
• Group work (2-3 students)
• Includes presentation and written report
• Due: End of term

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🤝 Contributing

This is an active teaching repository. Contributions are welcome:

1. Report Issues: Use GitHub Issues for bugs or suggestions
2. Submit Pull Requests: For typos, clarifications, or enhancements
3. Share Feedback: Contact the teaching team

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👥 Teaching Team

• Module Leader: Dr. Simone Santoni
• Teaching Assistants: See course website

Office Hours: Check the support page

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

Educational materials are provided for academic use. Please cite appropriately when using course materials in your work.

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🔗 Links

• Course Website: https://simonesantoni.github.io/net-analysis-smm638
• City, University of London: https://www.city.ac.uk
• Bayes Business School: https://www.bayes.city.ac.uk

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📧 Contact

For course-related queries:

• Email: simone.santoni.1@city.ac.uk
• Moodle: SMM638 course forum
• Office Hours: See support page

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⭐ Acknowledgments

• Built with Quarto
• Network analysis powered by igraph and NetworkX
• Visualization using ggraph and Plotly
• Hosted on GitHub Pages

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Last Updated: November 2025