Seidel adjacency matrix
Hi kiddo, today we're going to talk about a special kind of matrix called the Seidel Adjacency Matrix.
You know how when we draw lines between different things, we call them edges? A graph is just a bunch of points, or vertices, connected by lines, or edges. We can represent a graph using an adjacency matrix, which is a square matrix with 1's and 0's that shows whether each vertex is connected to another vertex.
Now, a Seidel Adjacency Matrix is a special kind of matrix that takes the adjacency matrix for a graph and adds a second set of edges connecting any two vertices that are not directly connected by an edge, but have exactly one common neighbor. This means that we can find out which vertices are "neighbors of neighbors".
Why do we need this kind of matrix, you ask? Well, it can be used to study things like social networks or how information spreads in a group of people. We can also use it to find things like the diameter of a graph, which is the longest shortest path between any two vertices.
So, in simpler terms, a Seidel Adjacency Matrix is like a map that helps us see all of the possible connections between different points, even if they are not directly connected. And we can use it to learn more about how things are connected in different networks. Cool, huh?
You know how when we draw lines between different things, we call them edges? A graph is just a bunch of points, or vertices, connected by lines, or edges. We can represent a graph using an adjacency matrix, which is a square matrix with 1's and 0's that shows whether each vertex is connected to another vertex.
Now, a Seidel Adjacency Matrix is a special kind of matrix that takes the adjacency matrix for a graph and adds a second set of edges connecting any two vertices that are not directly connected by an edge, but have exactly one common neighbor. This means that we can find out which vertices are "neighbors of neighbors".
Why do we need this kind of matrix, you ask? Well, it can be used to study things like social networks or how information spreads in a group of people. We can also use it to find things like the diameter of a graph, which is the longest shortest path between any two vertices.
So, in simpler terms, a Seidel Adjacency Matrix is like a map that helps us see all of the possible connections between different points, even if they are not directly connected. And we can use it to learn more about how things are connected in different networks. Cool, huh?