Graph Theory Algorithms for Competitive Programming from Udemy

Welcome to Graph Algorithms for Competitive Coding - the most detailed Specialisation in Graph Theory for Competitive Programmers, Software Engineers & Computer Science students.

Graphs is quite an important topic for software engineers, both for academics & online competitions and for solving real life challenges. Graph algorithms form the very fundamentals of many popular applications like - Google Maps, social media apps like Facebook, Instagram, Quora, LinkedIn, Computer Vision applications such as image segmentation, resolving dependencies while compile time, vehicle routing problems in supply chain and many more. This course provides a detailed overview of Graph Theory algorithms in computer science, along with hands on implementation of all the algorithms in C++. Not just that you will get 80+ competitive coding questions, to practice & test your skills.

This comprehensive course is taught by Prateek Narang & Apaar Kamal, who are Software Engineers at Google and have taught over thousands of students in competitive programming over last 5+ years. This course is worth thousands of dollars, but Coding Minutes is providing you this course to you at a fraction of its original cost. This is action oriented course, we not just delve into theory but focus on the practical aspects by building implementing algorithms & solving problems. With over 95+ high quality video lectures, easy to understand explanations this is one of the most detailed and robust course for Graph Algorithms ever created.Course starts very basics with how to store and represent graphs on a computer, and then dives into popular algorithms & techniques for problem solving. The course is divided into two parts.Part-I Graph Theory Essentials

Graph Representations
Popular Traversals - BFS & DFS
Cycle Detection - Weighted & Unweighted Graphs
Topological Ordering & Directed Acyclic Graphs
Disjoint Set Union, Path Compression & Union by Rank
Minimum Spanning Trees - Prim's & Kruskal's
Shortest Paths - BFS, Dijkstra's, Bellman Ford, Floyd Warshall
Travelling Salesman Problem, Min Cost Hamiltonian Cycle

Part-II Graph Theory Advanced

Flood Fill
Multisource BFS
DFS & Backedges
SCC's & Kosaraju's Algorithm
Euler Tour
LCA
Trees
Articulation Points & Bridges
Network Flow

The part-II is recommended for programmers who want to deep dive into Competitive Programming & take part in contests. For most students part-I is good enough to understand the most fundamental concepts and techniques in graphs. Our special thanks to our problem setters, Siddharth Singhal & Rajdeep from Delhi Technological University, who helped us crafting the complete problem-set for this course. So what you are waiting for ? Sign up today & start your deep-dive into graph theory.

What's inside

Learning objectives

Graph basics, applications
Bfs, dfs, connected components
Shortest paths - dijkstra, bellman, floyd warshall
Travelling salesman problem - dp with bitmasks
Topological ordering, strongly connected components
Disjoint set union, minimum spanning trees, prim's & kruskal
Advanced graphs, euler tour, trees

Network flow, lca, articulation points
Graphs for competitive programming
80 + competitive coding questions
Complete code repository in c++ and java
Coding exercises solutions
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Graph basics, applications
Bfs, dfs, connected components
Shortest paths - dijkstra, bellman, floyd warshall
Travelling salesman problem - dp with bitmasks
Topological ordering, strongly connected components
Disjoint set union, minimum spanning trees, prim's & kruskal
Advanced graphs, euler tour, trees
Network flow, lca, articulation points
Graphs for competitive programming
80 + competitive coding questions
Complete code repository in c++ and java
Coding exercises solutions
Show more
Show less

Syllabus

Introduction

Course Orientation!

Q/A Section & Discord Community

Graphs Code Repository C++ and Java!

Solutions to 80 Coding Questions.

Sharing Feedback

Setting Up Sublime [optional]

Sublime Setup

Adding Master Header File

Escaping Online Judges

Common Code Snippets

Using Macros

Example Code Explained

Learn about Graphs & their representation!

Graphs Introduction

Graph Applications

Graph Key Terms

Adjacency List Representation

Find Center of Star Graph

Adjacency List Representation with Node Class

Maximal Network Rank

Minimum Degree of a Connected Trio in a Graph

Some Helpful Webinars [Optional]

Breath First Search

Breadth First Search

BFS Code

BFS Shortest Path

BFS Shortest Path Code

Snakes and Ladder Game

Snakes and Ladder Solution

Message Route

Word Ladder

Valid Bfs?

Depth First Search

DFS Concept

DFS Code

Keys and Rooms

Largest Island

Largest Island Solution

Astronaut Pairs

Reconstruct Itinerary

Learn to solve problems involving Cycle Detection!

Cycle Detection in Undirected Graph

Cycle Detection in Undirected Graph Code

Directed Graph - Cycle Detection

Directed Graph Cycle Detection

Directed Graph - Cycle Detection Code

Course Schedule

Bipartite Graph

Bipartite Graph Code

Is Graph Bipartite?

Detecting an Odd Length Cycle

Detect Cycles in Grid

Shortest Cycle in Undirected Graph

Directed Acyclic Graph

Directed Acyclic Graph & Topological Ordering

Topological Sort Algorithm

Topological Ordering BFS Code

Toplogical Order using DFS

Topological Ordering using DFS Code

All Paths From Source to Target

Course Schedule II

Largest Color Value in a Directed Graph

Game Routes

Disjoint Set Union

Disjoint Set Union Introduction

DSU Data Structure - Union & Find Ops

DSU Data Structure

DSU Implementation

Forest Detection

Useless Connection

Union by Rank

Path Compression Optimisation

DSU Dry Run

Communication Between Towers

Make Network Connected

Special Paths

Minimum Spanning Trees

Introduction to Minimum Spanning Trees!

Prim's Algorithm

Prim's Code

Kruskal's Algorithm

Kruskal's Code

Minimum Spanning Cost

Connect All

Remove Maximum Number of Edges

Build Roads

Find Critical and Pseudo-Critical Edges in MST

Shortest Path Algorithms

Introduction to Shortest Path Algorithms

Dijkshtra's Algorithm

Dijkshtra's Algorithm Code

Dijkstra

Delay Time in Network

Bellman Ford Algorithm

Bellman Ford Code

Floyd Warshall

Floyd Warshall Code

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Taught by Google Software Engineers, which lends credibility and practical insights to the course content, especially for those seeking industry-relevant skills

Includes over 80 competitive coding questions, offering ample opportunity to practice and solidify understanding of graph algorithms, which is essential for competitive programming

Provides a comprehensive overview of graph theory algorithms, starting with basic concepts and progressing to advanced topics like network flow and LCA, which is suitable for various skill levels

Offers complete code repository in C++ and Java, which allows learners to implement and experiment with graph algorithms in multiple popular programming languages

Focuses on C++ implementations, which may require learners unfamiliar with the language to invest additional time in learning C++ alongside graph algorithms, potentially slowing their progress

Covers topics like Traveling Salesman Problem and Network Flow, which are computationally complex and may require a strong foundation in algorithms and data structures for effective comprehension

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Graph Theory Algorithms for Competitive Programming with these activities:

Review Data Structures

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Reviewing fundamental data structures will help you understand how graphs are represented and manipulated in code.

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Review the concept of adjacency lists.
Practice implementing graph representations.

Review 'Introduction to Algorithms' by Cormen et al.

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Referencing a comprehensive algorithms textbook will provide a solid theoretical foundation for the course.

View Introduction to Algorithms, fourth edition on Amazon

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Read the chapters on graph algorithms.
Work through the examples and exercises.

Solve Graph Problems on LeetCode

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Practicing graph problems on platforms like LeetCode will solidify your understanding and improve your problem-solving skills.

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Select graph problems of varying difficulty.
Implement solutions in C++ or Java.
Analyze the time and space complexity.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Review 'Competitive Programmer's Handbook' by Antti Laaksonen

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Studying a competitive programming handbook will provide practical strategies for solving graph problems in contests.

View Guide to Competitive Programming: Learning and... on Amazon

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Read the sections on graph algorithms and data structures.
Practice the example problems and exercises.

Create a Blog Post on a Graph Algorithm

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Writing a blog post explaining a graph algorithm will reinforce your understanding and help you communicate technical concepts effectively.

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Choose a graph algorithm from the course.
Research the algorithm thoroughly.
Write a clear and concise explanation.
Include examples and diagrams.

Implement a Graph Visualization Tool

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Building a graph visualization tool will deepen your understanding of graph representations and algorithms.

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Choose a programming language and visualization library.
Implement graph data structures.
Implement visualization algorithms.
Add features for user interaction.

Contribute to a Graph Algorithm Library

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Contributing to an open-source graph algorithm library will provide real-world experience and improve your coding skills.

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Find an open-source graph library on GitHub.
Identify a bug or missing feature.
Implement a solution or new feature.
Submit a pull request.

Career center

Learners who complete Graph Theory Algorithms for Competitive Programming will develop knowledge and skills that may be useful to these careers:

Competitive Programmer

Competitive programmers solve algorithmic problems under constraints, often in contests. This course is highly relevant, as it is specifically designed for competitive programmers. The course covers essential graph algorithms and techniques, such as graph representations, traversals, cycle detection, shortest paths, and minimum spanning trees. The 80+ competitive coding questions and complete code repository in C++ and Java can offer valuable practice and resources. This course helps competitive programmers to master graph theory and improve their problem-solving speed and accuracy.

See salaries and explore the career path for Competitive Programmer

Algorithm Developer

An algorithm developer creates and refines algorithms for various applications. This course directly prepares you for such a role, by improving your understanding of graph algorithms. The course's focus on graph theory fundamentals, traversal techniques, cycle detection, and shortest path algorithms are essential for designing efficient algorithms. The hands-on implementation of these algorithms in C++ and the numerous competitive coding questions provide algorithm developers with practical experience in problem-solving and optimization. This course may significantly contribute to your expertise as an algorithm developer.

See salaries and explore the career path for Algorithm Developer

Software Engineer

A software engineer designs, develops, tests, and maintains software applications. This course may provide a strong foundation for software engineers, especially those working on applications that rely heavily on graph theory. Knowledge of graph algorithms gained from this course helps in optimizing software performance, designing scalable systems, and solving complex problems using graphs. The hands-on implementation of algorithms in C++ and the practice with competitive coding questions can directly translate into better problem-solving skills and more efficient coding practices. Software engineers should consider this course to enhance their expertise in graph-related challenges. Topics such as shortest path algorithms and minimum spanning trees are particularly valuable.

See salaries and explore the career path for Software Engineer

Game Developer

Game developers create video games, often using pathfinding algorithms and other graph-related techniques. This course provides valuable knowledge for game developers, covering graph representations, traversals, cycle detection, and shortest path algorithms. These concepts are fundamental to implementing AI agents, designing game levels, and optimizing game performance. The hands-on implementation of algorithms in C++ and the problem-solving exercises may enhance a game developer's ability to create engaging and efficient games. The skills in this course are immediately applicable to game development.

See salaries and explore the career path for Game Developer

Network Engineer

Network engineers design, implement, and manage computer networks. This course may be useful for network engineers, especially those working on network routing and optimization. Graph algorithms such as shortest path algorithms and minimum spanning trees are used to optimize network traffic and ensure efficient communication between devices. This course provides a theoretical and practical understanding of these algorithms, which can be applied to network design and management. This knowledge is essential for optimizing network performance.

See salaries and explore the career path for Network Engineer

Research Scientist

A research scientist, often requiring a PhD, conducts research and develops new technologies. This course may be useful for research scientists who work on graph theory or related fields. The course provides a comprehensive overview of graph algorithms and techniques that can be applied to various research problems. The coverage of advanced topics such as network flow and Euler tours may be particularly valuable for research scientists working on cutting-edge graph-related research. This course helps bolster core theoretical knowledge.

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Robotics Engineer

Robotics engineers design, build, and program robots. This course may be useful for robotics engineers who work on path planning, navigation, and mapping. Graph algorithms such as Dijkstra's algorithm and A* search are commonly used in robotics to find optimal paths for robots to navigate through complex environments. The course may provide a solid foundation in graph theory and algorithms that can be applied to robotics problems. The topics concerning shortest path may be particularly helpful.

See salaries and explore the career path for Robotics Engineer

Data Scientist

A data scientist analyzes large datasets to extract meaningful insights and develop data-driven solutions. This course may be useful for data scientists working with graph-structured data. An understanding of graph algorithms such as network flow, shortest paths, and community detection is valuable for analyzing social networks, recommendation systems, and other complex relationships. The course provides a practical foundation in graph theory that can be applied to various data science problems. Although data scientists may not use all the techniques, they can apply the shortest path and data structure techniques to their work.

See salaries and explore the career path for Data Scientist

Operations Research Analyst

Operations research analysts, often requiring a master's degree, use mathematical and analytical methods to solve complex problems and optimize operations. This course may be useful for operations research analysts, especially those working on network optimization and logistics. Graph algorithms such as shortest path algorithms and network flow algorithms can be used to optimize transportation networks, supply chains, and other complex systems. The course may provide the necessary skills to improve efficiency.

See salaries and explore the career path for Operations Research Analyst

Cybersecurity Analyst

Cybersecurity analysts protect computer systems and networks from cyber threats. This course may be useful for cybersecurity analysts, especially those working on network security and vulnerability analysis. Graph algorithms can be used to analyze network traffic, detect anomalies, and identify potential security breaches. This course may provide a solid foundation in graph theory that can be applied to cybersecurity problems. For example, cybersecurity analysts can use graph algorithms to perform tasks.

See salaries and explore the career path for Cybersecurity Analyst

Artificial Intelligence Engineer

Artificial intelligence engineers design and build AI systems. This course may be useful for AI engineers who use graph-based approaches to solve problems. For example, graph search algorithms are often used in pathfinding and game playing. The course's coverage of graph representations, traversals, and shortest path algorithms may provide a solid foundation for implementing AI solutions that involve graphs. An AI engineer may find helpful background in these concepts.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. This course may be useful for machine learning engineers who work with graph-based machine learning techniques. Graph neural networks, graph embeddings, and other graph-based methods are increasingly used in various applications, such as social network analysis and recommendation systems. The course provides the necessary background in graph theory and algorithms to understand and implement these advanced techniques. A machine learning engineer may find the background helpful.

See salaries and explore the career path for Machine Learning Engineer

Database Administrator

Database administrators manage and maintain databases, often using graph databases to represent relationships between data. This course helps database administrators understand the underlying principles of graph theory, which are essential for designing and querying graph databases. Graph databases are increasingly used to model complex relationships in various domains. By taking this course database administrators can improve their ability to design and manage graph databases effectively. For those working with graph databases, this course may prove helpful.

See salaries and explore the career path for Database Administrator

Data Engineer

Data engineers build and maintain the infrastructure for data storage and processing. This course may be useful for data engineers who work with graph databases or graph processing frameworks. The course may help data engineers understand the underlying principles of graph theory and how to optimize graph data processing. The concepts and code examples may provide a valuable starting point for working with graph data at scale. A data engineer may find graph theory useful.

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Quantitative Analyst

Quantitative analysts, often requiring a master's degree, develop mathematical and statistical models for financial analysis. This course may be useful for quantitative analysts who work with network models or graph-based data. For example, graph theory can be used to analyze financial networks, detect fraud, and optimize trading strategies. Although this is not the focus, the course may enhance a quantitative analyst's ability to apply graph theory to financial problems. In some cases, this may be helpful.

See salaries and explore the career path for Quantitative Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Graph Theory Algorithms for Competitive Programming.

Introduction to Algorithms, fourth edition

Save

Comprehensive textbook on algorithms, covering a wide range of topics including graph algorithms. It provides detailed explanations, pseudocode, and analysis of various algorithms, making it an excellent resource for understanding the theoretical foundations. It is commonly used in undergraduate and graduate courses in computer science and valuable reference for competitive programmers.

Introduction to Algorithms, fourth edition

Hardcover

$$$$

Introduction to Algorithms, fourth edition

Kindle Edition

$$$

Guide to Competitive Programming

Save

Is specifically tailored for competitive programming and covers a wide range of algorithms and data structures, including graph algorithms. It provides concise explanations and efficient implementations of various algorithms, making it a practical guide for competitive programmers. The book also includes numerous examples and exercises from competitive programming contests.

Guide to Competitive Programming: Learning and...

Paperback

$$$

Graph Theory Algorithms for Competitive Programming

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