Federal University of Rio Grande do Norte

Technology Center

Department of Computer Engineering and Automation

Algorithms and Data Structure II (DCA3702)

This is the repository for the Algorithms and Data Structures II course, offered by the Department of Computer Engineering and Automation (DCA) of the Technology Center (CT) at the Federal University of Rio Grande do Norte (UFRN).

The DCA aims to train professionals capable of designing and developing computer systems for industrial automation, embedded systems, software systems, distributed systems, computer networks, and information systems. The CT offers undergraduate and graduate courses in Engineering. This course is part of the curriculum of the Computer Engineering program at DCA/UFRN.

References

Tool	Link
😃 Networkx	networkx.org
💡 Graph-Tool	graph-tool.skewed.de
⚙️ Gephi	gephi.org
🚀 OSMnx	github.com/gboeing/osmnx
💣 ChatGPT	chat.openai.com
💾 Dataset	snap.stanford.edu/data

Lessons

Week 01

Course Outline: Provides an overview of the course structure and topics covered.
- 🎯 Week Goals
  - Your main goal for this week is to create a personal repository for tracking your progress and coursework.
  - Feel free to explore these resources and engage with the course material. If you have any questions or need assistance, please don't hesitate to reach out.
  - Happy coding! 💻🚀
- 🎉 GitHub Education Benefits
  - GitHub Education Pro: Get access to the GitHub Education Pro pack by visiting GitHub Education
- 📖 Learning Resources
  - GitHub Learning Game: Check out the interactive Git learning game at GitHub Learning Game
  - Basic Python: Enhance your Python skills through the Kaggle Python course.
  - AI Python for Beginners: Learn Python programming fundamentals and how to integrate AI tools for data manipulation, analysis, and visualization. Andrew Ng
Network Fundamentals I: Outline, applications, math and graph theory.
- 🛠 Hands on
- Network elements using networkx
- 📚 Further reading: chapters 1 to 4 of the book The Atlas For The Aspiring Network Scientist, including the exercises.

Week 02

Network Fundamentals II: Probability, extended graphs, matrices, degree and representation.
- 📚 Further reading: chapters 5 to 6 of the book The Atlas For The Aspiring Network Scientist, including the exercises.

Week 03

Small World: This week, we explore key concepts such as Small World Networks, Homophily, and Assortativity. You will investigate how these concepts influence the structure and behavior of real-world networks.
- Hands on assortativity
- The art of seeing networks
- 📚 Further reading: chapters 26 to 27 of the book The Atlas For The Aspiring Network Scientist, including the exercises.

Week 04

Small World Cont.: Paths, Distances, Connected Components, Clustering Coefficient, Social Distance and Six Degrees of Separation
- Paths, Walks and Distances
- Connected Components
- Clustering Coefficient

Week 05

Hubs: the most important metrics and case studies.
- Eccentricity, Diameter, Periphery, Radius and Center
- Degree, Closeness, Betweenness and Eigenvector Centrality
- Centrality Distributions
- Core Decomposition
Hands on centralities measures

Week 06

Directed networks: case study of Wikipedia pages
- Building a networking from Wikipedia pages
- Collecting data from a snowballing process
- Truncate and eliminate duplicates
- Exploring the network data
- Wikipedia pages network

Week 07

Gephi - The Open Graph Viz Platform
- A brief overview about Gephi
- Quick start
- Using layouts
- Node and network measures
- Visualize and filtering nodes and communities
- Renderize, export the network, and highlight a community
- Deploy the network into an HTML page
- Another way to publish your network to the web using Retina and Gephisto

ivanovitchm/datastructure