English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 39 lectures (2h 21m) | 1.24 GB
Master NetworkX, Social Network Analysis & Shortest Path Algorithms – Build 4 Professional Projects with Graph Theory
Dive into the fascinating world of Graph Theory and its practical applications with this comprehensive, project-based course. Whether you’re a data scientist, software engineer, or algorithm enthusiast, you’ll learn how to solve real-world problems using graph algorithms in Python.
This course stands out by combining theoretical foundations with hands-on implementation, featuring four carefully designed projects that progressively build your expertise. You’ll start with the basics of graph theory and quickly advance to implementing sophisticated algorithms using NetworkX, Python’s powerful graph library.
Key features of this course include:
- Building a social network analyzer from scratch
- Implementing pathfinding algorithms for city navigation systems
- Designing optimal network infrastructure using MST algorithms
- Creating a professional recommendation system
You’ll master essential algorithms including Depth-First Search, Breadth-First Search, Dijkstra’s Algorithm, and advanced concepts like PageRank and community detection. Each topic is reinforced through practical exercises and real-world applications, from social media analysis to transportation network optimization.
The course includes complete Python implementations of all algorithms, with a focus on both efficiency and readability. You’ll learn industry best practices for working with NetworkX and visualization tools like Matplotlib, making your graph analysis both powerful and visually compelling.
Perfect for intermediate Python programmers who want to expand their algorithmic toolkit, this course requires basic Python knowledge but assumes no prior experience with graph theory or NetworkX. By the end, you’ll be able to analyze complex networks, optimize transportation systems, and build graph-based machine learning solutions.
Join us to transform your understanding of graph algorithms from theoretical concepts into practical, employable skills through hands-on projects and real-world applications.
What you’ll learn
- Master fundamental graph theory algorithms including DFS, BFS, Dijkstra’s Algorithm, and implement them efficiently using Python and NetworkX
- Build a complete social network analyzer from scratch, including visualization tools and community detection algorithms
- Implement and optimize pathfinding algorithms for real-world applications like city navigation systems and transportation networks
- Design and develop optimal network infrastructure using Minimum Spanning Tree algorithms (Kruskal’s and Prim’s)
- Create professional graph visualizations using NetworkX and Matplotlib, including interactive network displays and analysis tools
- Apply centrality measures and PageRank algorithms to analyze influence and importance in social networks
- Develop a recommendation system using graph-based algorithms and machine learning techniques
- Master advanced network analysis techniques including community detection, bipartite graphs, and articulation points
- Build four complete real-world projects that demonstrate practical applications of graph theory in modern software development
Table of Contents
Introduction to Graph Theory and Python for Graphs
1 What is Graph Theory (Brief Overview)
2 Types of Graphs (Directed, Undirected, Weighted)
3 Introduction to Python for Graphs
4 Working with NetworkX for Graph Creation
Social Network Representation (project1)
5 Creating a Simple Social Network Graph
6 Adding Nodes and Edges
7 Visualizing the Graph using Matplotlib
8 Analysis of Basic Graph Properties (Degree, Path Length)
Graph Traversal Algorithms
9 Depth-First Search (DFS)
10 Breadth-First Search (BFS)
11 Recursive vs Iterative Implementations
12 Application Graph Exploration
Shortest Path in a City Map (project 2)
13 Representing a City Map as a Graph
14 Implementing Dijkstra’s Algorithm to Find Shortest Paths
15 Visualizing the Path with Weights
16 Analyzing the Performance of the Algorithm
Graph Search and Connectivity
17 Connected Components
18 Articulation Points and Bridges
19 Bipartite Graphs
20 Real-World Application Network Resilience
Minimum Spanning Tree (MST) Algorithms
21 Kruskal’s Algorithm
22 Prim’s Algorithm
23 Applications of MST in Network Design
24 Implementing MST Algorithms in Python
Designing an Optimal Network (project3)
25 Creating a Network for Fiber Optic Cable Installation
26 Applying MST Algorithms (Prim’s and Kruskal’s)
27 Visualizing the Optimal Network Design
28 Cost Analysis and Efficiency
Graph Algorithms for Social Networks
29 Centrality Measures (Degree, Betweenness, Closeness)
30 Community Detection Algorithms
31 PageRank Algorithm
32 Graph-Based Applications in Social Media
Graph Algorithms in Real-World Applications
33 Graph-Based Machine Learning
34 Graphs in Biology
35 Graphs in Transportation and Networks
36 Graphs in Search Engines
End-of-course Projects
37 Graph-Based Recommendation System
38 Advanced Network Flow Optimization
39 Social Network Analysis Project
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