Modern Graph Theory Algorithms With Python 2025

Master NetworkX, Social Network Analysis & Shortest Path Algorithms - Build 4 Professional Projects with Graph Theory

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

Requirements

Basic Python programming experience (variables, functions, loops, and basic data structures). No advanced Python knowledge required

Basic understanding of data structures (arrays, lists, dictionaries). No prior graph theory knowledge needed

Python 3.x installed on your computer (Windows, Mac, or Linux)

Familiarity with using pip to install Python packages (we'll guide you through installing NetworkX and Matplotlib)

Basic math skills (high school level algebra). No advanced mathematics required

A computer with minimum 4GB RAM and any modern operating system

Text editor or IDE of your choice (we recommend VS Code, but any will work)

Enthusiasm to learn about networks and graph algorithms - perfect for beginners in graph theory!

Description

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 scratchImplementing pathfinding algorithms for city navigation systemsDesigning optimal network infrastructure using MST algorithmsCreating a professional recommendation systemYou'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.

Overview

Section 1: Introduction to Graph Theory and Python for Graphs

Lecture 1 What is Graph Theory? (Brief Overview)

Lecture 2 Types of Graphs (Directed, Undirected, Weighted)

Lecture 3 Introduction to Python for Graphs

Lecture 4 Working with NetworkX for Graph Creation

Section 2: Social Network Representation (project1)

Lecture 5 Creating a Simple Social Network Graph

Lecture 6 Adding Nodes and Edges

Lecture 7 Visualizing the Graph using Matplotlib

Lecture 8 Analysis of Basic Graph Properties (Degree, Path Length)

Section 3: Graph Traversal Algorithms

Lecture 9 Depth-First Search (DFS)

Lecture 10 Breadth-First Search (BFS)

Lecture 11 Recursive vs Iterative Implementations

Lecture 12 Application: Graph Exploration

Section 4: Shortest Path in a City Map (project 2)

Lecture 13 Representing a City Map as a Graph

Lecture 14 Implementing Dijkstra’s Algorithm to Find Shortest Paths

Lecture 15 Visualizing the Path with Weights

Lecture 16 Analyzing the Performance of the Algorithm

Section 5: Graph Search and Connectivity

Lecture 17 Connected Components

Lecture 18 Articulation Points and Bridges

Lecture 19 Bipartite Graphs

Lecture 20 Real-World Application: Network Resilience

Section 6: Minimum Spanning Tree (MST) Algorithms

Lecture 21 Kruskal’s Algorithm

Lecture 22 Prim’s Algorithm

Lecture 23 Applications of MST in Network Design

Lecture 24 Implementing MST Algorithms in Python

Section 7: Designing an Optimal Network (project3)

Lecture 25 Creating a Network for Fiber Optic Cable Installation

Lecture 26 Applying MST Algorithms (Prim’s and Kruskal’s)

Lecture 27 Visualizing the Optimal Network Design

Lecture 28 Cost Analysis and Efficiency

Section 8: Graph Algorithms for Social Networks

Lecture 29 Centrality Measures (Degree, Betweenness, Closeness)

Lecture 30 Community Detection Algorithms

Lecture 31 PageRank Algorithm

Lecture 32 Graph-Based Applications in Social Media

Section 9: Graph Algorithms in Real-World Applications

Lecture 33 Graph-Based Machine Learning

Lecture 34 Graphs in Biology

Lecture 35 Graphs in Transportation and Networks

Lecture 36 Graphs in Search Engines

Section 10: End-of-course Projects

Lecture 37 Graph-Based Recommendation System

Lecture 38 Advanced Network Flow Optimization

Lecture 39 Social Network Analysis Project

Python developers who want to expand their skills into graph theory and network analysis, especially those interested in building practical applications,Data Scientists and Analysts looking to master network visualization and graph-based algorithms for complex data analysis and machine learning,Computer Science students or self-learners who want hands-on experience implementing graph algorithms beyond theoretical classroom knowledge,Software Engineers working with network systems, social platforms, or recommendation engines who need practical graph algorithm implementation skills,IT Professionals seeking to understand network optimization and analysis through modern Python tools and libraries,Tech professionals transitioning into roles involving social network analysis, route optimization, or network infrastructure design