Master practical network science techniques for analyzing social networks, knowledge graphs, and biological networks systematically from fundamentals
Series Overview
This series is a practical educational content consisting of 5 chapters that allows you to learn the theory and implementation of Network Analysis from fundamentals step by step.
Network Analysis is a technique for extracting patterns and relationships from structural data represented by nodes (vertices) and edges. You will systematically learn a wide range of analytical methods, from graph theory fundamentals to node importance evaluation using centrality measures (degree centrality, betweenness centrality, PageRank), community structure discovery through community detection (Louvain method, Label Propagation), and intuitive understanding through network visualization. It is utilized in diverse fields including social network analysis (influencer discovery on SNS, information diffusion prediction), knowledge graphs (entity relationship analysis, reasoning), biological networks (protein interactions, gene regulatory networks), and recommender systems (collaborative filtering, user-item relationships). You will understand and be able to implement network analysis technologies that companies like Google (PageRank), Facebook (social graph analysis), and Amazon (recommender systems) have put into practical use. This series provides practical knowledge using major tools such as NetworkX, igraph, and Gephi.
Features:
- ✅ From Theory to Implementation: Systematic learning from graph theory fundamentals to advanced community detection
- ✅ Implementation-Focused: 40+ executable Python/NetworkX/igraph code examples and practical techniques
- ✅ Intuitive Understanding: Understand principles through network visualization and metric interpretation
- ✅ Latest Technology Compliance: Implementation using NetworkX, igraph, and Gephi
- ✅ Practical Applications: Application to social network analysis, knowledge graphs, and recommender systems
Total Study Time: 100-120 minutes (including code execution and exercises)
How to Learn
Recommended Learning Order
For Beginners (completely new to network analysis):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Time Required: 100-120 minutes
For Intermediate Learners (with graph theory experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Time Required: 75-90 minutes
Reinforcement of Specific Topics:
- Graph Theory Fundamentals: Chapter 1 (focused study)
- Centrality Measures: Chapter 2 (focused study)
- Community Detection: Chapter 3 (focused study)
- Visualization & Tools: Chapter 4 (focused study)
- Practical Applications: Chapter 5 (focused study)
- Time Required: 20-25 minutes/chapter
Chapter Details
Chapter 1: Network Analysis Basics
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Graph Theory Fundamentals - Nodes, edges, directed graphs, undirected graphs
- Network Representation - Adjacency matrix, adjacency list, edge list
- Basic Metrics - Degree, Density, Diameter
- NetworkX Introduction - Graph construction, basic operations, adding attributes
- Small-Scale Network Analysis - Karate Club, Les Misérables
Learning Objectives
- ✅ Understand basic concepts of graph theory
- ✅ Explain mathematical representations of networks
- ✅ Calculate basic metrics such as degree and density
- ✅ Construct and manipulate graphs with NetworkX
- ✅ Analyze networks from real data
Chapter 2: Centrality Measures
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Degree Centrality - Importance evaluation by connection count
- Betweenness Centrality - Importance of information transmission paths
- Closeness Centrality - Proximity to all nodes
- Eigenvector Centrality - Connections to important nodes
- PageRank - Google's search algorithm, weighted importance
Learning Objectives
- ✅ Understand definitions and meanings of each centrality measure
- ✅ Select appropriate metrics according to tasks
- ✅ Calculate and compare centrality measures
- ✅ Implement PageRank algorithm
- ✅ Identify influential nodes
Chapter 3: Community Detection
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- What is Community Detection - Discovery of densely connected subgraphs, clustering
- Louvain Method - Modularity maximization, hierarchical community detection
- Label Propagation - Label propagation, fast community detection
- Girvan-Newman Method - Division by edge betweenness, hierarchical method
- Modularity - Evaluation metric for community quality
Learning Objectives
- ✅ Understand the purpose of community detection
- ✅ Discover communities using Louvain method
- ✅ Explain characteristics of each algorithm
- ✅ Evaluate communities using modularity
- ✅ Analyze group structures in real networks
Chapter 4: Network Visualization and Analysis Tools
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Visualization with NetworkX - Layout algorithms, styling
- Utilizing igraph - Fast large-scale graph analysis, C/C++ based
- How to Use Gephi - Interactive visualization, export
- Visualization Techniques - Node size, color coding, edge thickness
- Interactive Visualization - PyVis, Plotly, dynamic networks
Learning Objectives
- ✅ Effectively visualize networks with NetworkX
- ✅ Analyze large-scale graphs quickly with igraph
- ✅ Create interactive visualizations with Gephi
- ✅ Select visualization methods according to purposes
- ✅ Communicate analysis results visually
Chapter 5: Applications of Network Analysis
Difficulty: Intermediate
Reading Time: 20-25 minutes
Code Examples: 8
Learning Content
- Social Network Analysis - Influencer discovery, information diffusion models
- Knowledge Graph Analysis - Entity relationships, reasoning, link prediction
- Biological Networks - Protein interactions, gene regulatory networks
- Recommender Systems - Collaborative filtering, user-item graphs
- Link Prediction - Common neighbor nodes, Adamic-Adar index, machine learning
Learning Objectives
- ✅ Discover influencers from social networks
- ✅ Analyze relationships in knowledge graphs
- ✅ Understand and analyze biological networks
- ✅ Implement graph-based recommender systems
- ✅ Predict network evolution using link prediction
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain fundamentals of graph theory and network science
- ✅ Understand meanings and appropriate use of each centrality measure
- ✅ Explain mechanisms of community detection algorithms
- ✅ Understand principles and methods of network visualization
- ✅ Explain the role of network analysis in various application domains
Practical Skills (Doing)
- ✅ Construct and analyze networks with NetworkX/igraph
- ✅ Calculate centrality measures and identify important nodes
- ✅ Discover group structures through community detection
- ✅ Create effective network visualizations
- ✅ Predict future connections using link prediction
Application Ability (Applying)
- ✅ Apply appropriate network analysis to business challenges
- ✅ Extract valuable insights from social networks
- ✅ Analyze complex relationships in knowledge graphs
- ✅ Design network-based recommender systems
- ✅ Create practical network analysis pipelines for real work
Prerequisites
To learn this series effectively, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Fundamentals: Variables, functions, classes, loops, conditional statements
- ✅ NumPy Fundamentals: Array manipulation, matrix operations, basic linear algebra
- ✅ Machine Learning Fundamentals: Unsupervised learning, clustering concepts (recommended)
- ✅ Data Visualization: Matplotlib, basic graph creation
- ✅ Linear Algebra Fundamentals: Matrix operations, eigenvalues and eigenvectors (recommended)
Recommended (Nice to Have)
- 💡 Graph Theory: Basic graph concepts (can be learned automatically)
- 💡 Statistics Fundamentals: Distributions, correlation, statistical testing
- 💡 Algorithms and Data Structures: Search algorithms, shortest paths
- 💡 GNN (Graph Neural Networks): Graph deep learning (advanced learning)
Recommended Prior Learning:
- 📚 - ML fundamentals
- 📚 Unsupervised Learning Introduction - Clustering
- 📚 - NumPy, pandas
- 📚 GNN Introduction Series (ML-A05) - Graph deep learning (recommended)
Technologies and Tools Used
Major Libraries
- NetworkX 3.1+ - Python graph library, diverse algorithms
- igraph 0.10+ - High-speed graph processing, C/C++ based
- NumPy 1.24+ - Numerical computation, matrix operations
- pandas 2.0+ - Data manipulation, graph data organization
- Matplotlib 3.7+ - Basic network visualization
- scikit-learn 1.3+ - Clustering, evaluation metrics
Visualization Tools
- Gephi 0.9.7+ - Interactive network visualization
- PyVis 0.3+ - Python interactive visualization
- Plotly 5.15+ - Web-enabled visualization
- Cytoscape 3.9+ - Biological network visualization
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook / Lab - Interactive development environment
- Google Colab - Cloud execution environment (free to use)
Datasets
- Zachary's Karate Club - Educational social network
- Les Misérables - Character relationship network from literature
- Facebook Ego Networks - Social network
- Cora / CiteSeer - Paper citation networks
- Protein-Protein Interaction (PPI) - Biological network
Let's Get Started!
Are you ready? Start with Chapter 1 and master network analysis techniques!
Chapter 1: Network Analysis Basics →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Deep Dive Learning
- 📚 Graph Neural Networks (GNN): Graph deep learning, GCN, GAT
- 📚 Dynamic Network Analysis: Time-evolving networks, change detection
- 📚 Large-Scale Graph Processing: GraphX, distributed graph processing, scalability
- 📚 Graph Mining: Frequent pattern discovery, graph classification
Related Series
- 🎯 Graph Neural Networks (GNN) Introduction (ML-A05) - Graph deep learning
- 🎯 - Knowledge reasoning, relation extraction
- 🎯 - Graph-based recommendation
Practical Projects
- 🚀 Social Network Influence Analysis - Twitter/SNS data analysis
- 🚀 Paper Recommendation System - Citation networks and link prediction
- 🚀 Knowledge Graph Construction - Relation extraction from Wikipedia
- 🚀 Protein Interaction Analysis - Analysis of biological networks
Update History
- 2025-10-23: v1.0 Initial release
Your network analysis journey starts here!