In graph theory and computer science, an adjacency matrix is a square matrix used to represent a finite graph. The elements of the matrix indicate whether pairs of vertices are adjacent or not in the graph. In the special case of a finite simple graph, the adjacency matrix is a (0,1)-matrix with zeros on its diagonal. If the graph is undirected (i.e. all of its edges are bidirectional), the adjacency matrix is symmetric. The relationship between a graph and the eigenvalues and eigenvectors of its adjacency matrix is studied in spectral graph theory. The adjacency matrix of a graph should be distinguished from its incidence matrix, a different matrix representation whose elements indicate whether vertex–edge pairs are incident or not, and its degree matrix, which contains information about the degree of each vertex. (Wikipedia).

How do we represent graphs using adjacency matrices? That is the subject of today's graph theory lesson! We will take a graph and use an adjacency matrix to represent it! It is a most soulless, but at times useful, graph representation. An adjacency matrix has a row and a column for each

From playlist Graph Theory

Graph Representation part 02 - Adjacency Matrix

See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have talked about Adjacency Matrix representation of Graph and analyzed its time and space complexity of adjacency matrix representation. Previous Less

From playlist Data structures

In this video, I define the notion of adjugate matrix and use it to calculate A-1 using determinants. This is again beautiful in theory, but inefficient in examples. Adjugate matrix example: https://youtu.be/OFykHi0idnQ Check out my Determinants Playlist: https://www.youtube.com/playlist

From playlist Determinants

Section 3b Adjacency Matrix and Incidence Matrix

From playlist Graph Theory

Matrices | Adjoint of a Matrix | Don't Memorise

From playlist Matrices

Matrices | Adjoint of a Matrix (Examples) | Don't Memorise

From playlist Matrices

We show the connection between the method of adjoints in optimal control to the implicit function theorem ansatz. We relate the costate or adjoint state variable to Lagrange multipliers.

Graph Theory: 07 Adjacency Matrix and Incidence Matrix

The adjacency matrix of a graph and the incidence matrix of a graph are two ways to contain all of the information about the graph in a very useful format. Here we define these two types of matrices and show how to build them with an example. Also includes BONUS FOOTAGE explaining how to

From playlist Graph Theory part-1

Simple Message Passing on Graphs

Join my FREE course Basics of Graph Neural Networks (https://www.graphneuralnets.com/p/basics-of-gnns/?src=yt)! This video discusses the adjacency matrix and how it can be used to implement basic message passing on graphs. A simple example is given using Python. Code: https://github.co

From playlist Graph Neural Networks

This is Lecture 11 of the CSE373 (Analysis of Algorithms) course taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Stony Brook University in 2007. The lecture slides are available at: http://www.cs.sunysb.edu/~algorith/video-lectures/2007/lecture11.pdf More informa

From playlist CSE373 - Analysis of Algorithms - 2007 SBU

Extremal Combinatorics with Po-Shen Loh - 04/20 Mon

Carnegie Mellon University is protecting the community from the COVID-19 pandemic by running courses online for the Spring 2020 semester. This is the video stream for Po-Shen Loh’s PhD-level course 21-738 Extremal Combinatorics. Professor Loh will not be able to respond to questions or com

From playlist CMU PhD-Level Course 21-738 Extremal Combinatorics

Intro to graph neural networks (ML Tech Talks)

In this session of Machine Learning Tech Talks, Senior Research Scientist at DeepMind, Petar Veličković, will give an introductory presentation and Colab exercise on graph neural networks (GNNs). Chapters: 0:00 - Introduction 0:34 - Fantastic GNNs and where to find them 7:48 - Graph data

From playlist ML & Deep Learning

Lecture 10 - Graph Data Structures

This is Lecture 10 of the CSE373 (Analysis of Algorithms) taught by Professor Steven Skiena [http://www.cs.sunysb.edu/~skiena/] at Stony Brook University in 1997. The lecture slides are available at: http://www.cs.sunysb.edu/~algorith/video-lectures/1997/lecture14.pdf

From playlist CSE373 - Analysis of Algorithms - 1997 SBU

Graph Representation part 03 - Adjacency List

See complete series on data structures here: http://www.youtube.com/playlist?list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P In this lesson, we have talked about Adjacency List representation of Graph and analyzed its time and space complexity of adjacency list representation. Previous Lesson:

From playlist Data structures

What is a Matrix?

What is a matrix? Free ebook http://tinyurl.com/EngMathYT

From playlist Intro to Matrices