Can graph spectrums be derived from incident matrices?

In summary, the conversation discusses the use of eigenvalues in determining the graph spectrum for both adjacency and incident matrices. While the set of eigenvalues of an adjacency matrix (sa) is well-defined, the set of eigenvalues of an incident matrix (si) may not be applicable as it is not a square matrix. However, the singular values of an incident matrix may be related to the eigenvalues of the line graph of the given graph.
  • #1
1Truthseeker
43
0
Will the set of eigenvalues of an incident matrix derive an equivalent notion of a graph spectrum as it does with an adjacency matrix?

Specifically:

Let sa be the set of eigenvalues of an adjacency matrix for graph G.

And,

Let si be the set of eigenvalues of an incident matrix for graph G.

What are the differences between sa and si? Could these both be considered spectrums of the graph? If not, why?

Thanks much!
 
Physics news on Phys.org
  • #2
What is your definition for incident matrix? If it's the usual definition, then it's likely not a square (nxn) matrix so it doesn't make sense to talk about eigenvalues. You can however look at its singular values which I think are related to eigenvalues of the line graph of G.
 

1. Can graph spectrums be derived from incident matrices?

The short answer is yes, graph spectrums can be derived from incident matrices. This is because the incident matrix of a graph contains information about the edges and vertices of the graph, and the spectrum of a graph is a set of numbers that represent the eigenvalues of the graph's adjacency matrix.

2. Why is it useful to derive graph spectrums from incident matrices?

Deriving graph spectrums from incident matrices can help us better understand the properties and behavior of a graph. The spectrum can reveal important information about the graph, such as its connectivity, diameter, and symmetry. It can also be used for graph classification and comparison purposes.

3. Are there any limitations to deriving graph spectrums from incident matrices?

Yes, there are some limitations to this method. For example, it may not work for graphs with certain types of structures or symmetries. It also may not provide a complete understanding of the graph, as other graph properties may not be reflected in the spectrum.

4. What are some applications of deriving graph spectrums from incident matrices?

This method has various applications in fields such as network analysis, chemistry, and physics. It can be used to study the behavior of complex networks, analyze molecular structures, and understand the properties of physical systems represented by graphs.

5. Are there any alternative methods for deriving graph spectrums?

Yes, there are other methods for deriving graph spectrums, such as using the Laplacian matrix or the signless Laplacian matrix. Each method has its own advantages and limitations, and the choice of method may depend on the specific application or research question.

Similar threads

  • Linear and Abstract Algebra
Replies
8
Views
1K
Replies
13
Views
2K
Replies
12
Views
3K
  • Calculus and Beyond Homework Help
Replies
1
Views
704
  • Linear and Abstract Algebra
Replies
4
Views
3K
  • Programming and Computer Science
Replies
8
Views
1K
  • Linear and Abstract Algebra
Replies
1
Views
10K
Replies
4
Views
1K
  • Math Proof Training and Practice
Replies
2
Views
2K
  • Linear and Abstract Algebra
Replies
4
Views
2K
Back
Top