Associate Professor
Network Modeling and Analysis in Python
What You'll Learn
- Understand the fundamental principles underlying network structures and apply NetworkX to analyze these principles in real-world networks.
- Describe the practical uses of network generation models, and learn to apply various such models to create synthetic networks.
- Identify several basic diffusion models and use them to run simulations using real and synthetic networks.
- Describe the practical uses of the community detection problem and use algorithms to detect and evaluate community structure in real networks.
4 Modules
28 Hours
7 hrs per module (approx.)
Rating
About Network Modeling and Analysis in Python
In “Network Modeling and Analysis in Python,” you will learn how different types of network analysis can be used to make sense of complex systems. You’ll learn how algorithms can be used to better understand disease epidemics, human community structure, and the flow of information on social media. This course combines network theory with empirical analysis of real-world networks using the Python library NetworkX. You’ll learn about community structure in networks as well as several popular algorithms for community detection and applications.
This course introduces a wide range of advanced network models. You’ll study random network generation models and how they can be used to create realistic graphs and explain how networks function. You’ll also learn about models that explain diffusion and the spread of epidemics in networks, such as the SI, SIS, SIR, independent cascade, and linear threshold models.
This is the third course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.
Skills You'll Gain
- Data Analysis
- Probability Distribution
- Python (Programming Language)
What You'll Earn
- Certificate of Completion:
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Network Modeling and Analysis in Python, a course focused on understanding and modeling complex networks. Learners analyze real-world network data, explore community structure, generative models, and diffusion processes, and identify influential nodes using Python tools.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: Connectivity in Networks
- Video: Welcome to Network Modeling and Analysis in Python
- Reading: MADSwPy Certificate Roadmap
- Reading: Course Syllabus
- Reading: Introduction to Jupyter Notebook
- Discussion Prompt: Meet Other Learners
- Reading: Help Us Learn About You
- Video: Homophily and Assortativity
- Video: Structural Holes
- Video: K-core Decomposition
- Graded Assignment: Knowledge Check: Connectivity in Networks
- Video: Demo: Connectivity NetworkX Tutorial
- Ungraded Lab: Module 1 NetworkX Tutorial
- Reading: Optional Readings and Resources
- Reading: Review of Key Concept
- Reading: Network Definitions and Vocabulary
- Reading: Network Connectivity
- Reading: Influence Measure and Network Centralization
- Reading: Network Evolution
- Graded Assignment: Optional-Review of Key Concepts
- Graded: Module 1 Assignment
- Graded: Module 1 Programming Assignment: Connectivity in Networks
Module 2: Community Structure in Networks
- Video: What is Community Detection?
- Video: Modularity
- Graded Assignment: Knowledge Check: Introduction to Community Detection
- Video: The Girvan-Newman Algorithm
- Video: Agglomerative Hierarchical Clustering
- Video: Label Propagation with Asynchronous Updating
- Video: Label Propagation with Synchronous Updating
- Graded Assignment: Knowledge Check: Community Detection Algorithms
- Role Play: Communities in Networks
- Video: Assessing the Quality of a Partition
- Graded Assignment: Knowledge Check: Assessment and Applications
- Video: Demo: Community Structure NetworkX Tutorial
- Ungraded Lab: NetworkX Tutorial 2
- Reading: Optional Readings and Resources
- Graded: Module 2 Assignment
- Graded: Module 2 Programming Assignment: Community Structure in Networks
Module 3: Network Generative Models
- Video: Introduction to Network Generative Models
- Video: The Erdős–Rényi Model
- Graded Assignment: Knowledge Check: The Erdős–Rényi Model
- Video: The Stochastic Block Model
- Video: The Configuration Model
- Graded Assignment: Knowledge Check: The Stochastic Block Model and The Configuration Model
- Video: Demo: Generative Models NetworkX Tutorial
- Ungraded Lab: Module 3 NetworkX Tutorial
- Reading: Optional Readings and Resources
- Graded: Module 3 Assignment
- Graded: Module 3 Programming Assignment
Module 4: Models of Diffusion in Networks
- Video: Introduction to Diffusion in Networks
- Video: The SIS Model
- Video: The SIR Model
- Graded Assignment: Knowledge check: Basic Models of Diffusion in Networks (Part 1)
- Video: The Independent Cascade Model
- Video: The Linear Threshold Model (Part 1)
- Video: The Linear Threshold Model (Part 2)
- Graded Assignment: Knowledge Check: Basic Models of Diffusion in Networks (Part 2)
- Reading: Honduras Social Networks
- Video: Introduction to the Influence Maximization Problem
- Video: Properties of the Influence Maximization Problem
- Video: Greedy Algorithm for the Influence Maximization Problem
- Video: Heuristics and Nomination Strategy
- Video: Applications of Community Detection
- Graded Assignment: Knowledge Check: The Influence Maximization Problem
- Video: Demo: Diffusion NetworkX Tutorial
- Ungraded Lab: Module 4 NetworkX Tutorial
- Reading: Optional Readings and Resources
- Graded: Module 4 Assignment
- Graded: Module 4 Programming Assignment: Models of Diffusion on Networks
Grading Policy
There are four graded assignments in this course that make up 28% of your final grade. There are also four programming assignments that make up 72% of your final grade.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Advanced Level
Learners will benefit from a background in linear algebra, and it is recommended that they proceed through this series in the suggested order.
