Adjacency matrix and probability matrix

In summary, the conversation discusses the relationship between a k-regular simple graph and its directed double, and how the matrix S (the probability matrix) for the former is a multiple of the adjacency matrix T for the latter. The factor that maps T to S is 1/k, as proven by the preservation of adjacency in both matrices and their differing nonzero entries by a factor of 1/k. This factor is also supported by the fact that in S, the nonzero entries represent the probability of going from one vertex to another adjacent vertex, which is always 1/k due to the k lines leaving each node.
  • #1
TheMathNoob
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4

Homework Statement


If Γ is a k-regular simple graph and Γ its directed double, show that the matrix ˜ S for Γ (as per the FEATURED ARTICLE ) is a multiple of the adjacency matrix ˜ for Γ. Find the multiple. Assume k > 1.
The matrix S is the probability matrix. The probability of going from one node to another node based on the number of lines coming out of each node.

R

Homework Equations

The Attempt at a Solution


So it looks like the factor that maps T->S is 1/k so T*1/k=S.
Proof
This makes sense because the idea of adjacency is preserved on both matrices and their nonzero entries differ by a factor of 1/k because in S each nonzero entry represents the probability of going from one vertex to another adjacent vertex. In this case, it will always be 1/k because the number of lines leaving each node is always k. In T we just assert the adjacency between vertices by putting a 1.

That's a good start. I don't how to set this up, so it proves that the factor is 1/k
 
Last edited:
  • #3
The phrase "as per the featured article" indicates that there is more to this problem that you have not told us!
 

Related to Adjacency matrix and probability matrix

What is an adjacency matrix?

An adjacency matrix is a square matrix that is used to represent the connections or relationships between a set of vertices in a graph. It is typically used to represent directed or undirected graphs.

How is an adjacency matrix constructed?

An adjacency matrix is constructed by assigning a value of 1 to the corresponding row and column if there is an edge connecting the two vertices, and a value of 0 if there is no edge. For directed graphs, the direction of the edge can also be represented by using a different value (e.g. -1 for one direction and 1 for the other).

What is the significance of an adjacency matrix?

An adjacency matrix is significant because it allows for efficient storage and manipulation of graph data. It also allows for quick retrieval of information about the connections between vertices, such as finding the neighbors of a particular vertex.

What is a probability matrix?

A probability matrix is a square matrix that contains probabilities as its elements. It is commonly used in fields such as statistics and machine learning to represent the probabilities of different events or outcomes.

How are adjacency and probability matrices related?

An adjacency matrix can be converted into a probability matrix by dividing each element by the sum of the elements in its row. This allows for the representation of the probabilities of transitioning from one vertex to another in a graph.

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