Properties of adjacency matrix of graph with cycle

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Discussion Overview

The discussion revolves around the properties of the adjacency matrix of a graph, particularly focusing on the implications of cycles in the graph on the matrix (I-A)-1. Participants explore the counting of paths between vertices and the significance of singular versus non-singular matrices in this context.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant notes that (I-A)-1 counts the total number of paths between vertices in graphs without cycles and non-singular A, questioning the applicability when cycles are present.
  • Another participant challenges the necessity of A being non-singular, suggesting that I-A must be non-singular instead, and provides examples of singular matrices in specific graph cases.
  • A different approach is proposed where instead of using 1 in the adjacency matrix, participants suggest using 1-ε to analyze the convergence of paths of length n, raising questions about the utility of this method.
  • One participant shares their experimentation with the K2 graph using the modified adjacency matrix, noting that the off-diagonal terms in the resulting matrix are negative and expressing uncertainty about its interpretation.

Areas of Agreement / Disagreement

Participants express differing views on the necessity of non-singularity of A versus I-A, and there is no consensus on the implications of using (I-A)-1 in graphs with cycles. The utility of modifying the adjacency matrix with 1-ε also remains uncertain among participants.

Contextual Notes

Participants highlight limitations regarding the assumptions of non-singularity and the specific types of graphs being discussed, as well as the potential for divergent interpretations of the modified adjacency matrix.

asja
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Let A be the adjacency matrix of some graph G. I am aware that A^n
counts paths of length n between vertices of G, and that for graphs
without cycles and non-singular A, (I-A)^-1 counts the total number of
paths between vertices of G (correct me if any of this is wrong).This
is a very limited class of graph however and I was wondering whether
there is any useful information at all that can be obtained from the
matrix (I-A)^-1 when A is non-singular and G contains a cycle (from
the entries, determinant, etc.)? To take this further, what about if
the matrix is singular? Is there any information that can be extracted
other than counting paths of length n for each n?
Thanks for any information.
 
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asja said:
for graphs
without cycles and non-singular A, (I-A)^-1 counts the total number of
paths between vertices of G
Why do we need A non-singular? We certainly need I-A non-singular. E.g. for a null graph A is singular but (I-A)-1-I correctly gives zero paths for each pair.
And presumably this is for directed graphs without cycles; otherwise it seems it is going to count bouncing back and forth between two adjacent vertices as an infinite number of paths of even length.
I would think I-A is non-singular if and only if it is a directed graph without cycles. E.g. for a simple directed triangle on three vertices:
##\begin{pmatrix} 1&-1&0\\0&1&-1\\-1&0&1\end{pmatrix}##
is indeed singular, and likewise for K2:
##\begin{pmatrix} 1&-1\\-1&1\end{pmatrix}##
 
A hacks thing you can do, is instead of writing 1, write ##1-\epsilon## in the adjacency matrix
Then each entry of ##A^n## is ##k(1-\epsilon)^n## where ##k## is the number of paths of length ##n##. As long as the number of paths doesn't grow exponentially in n this sum should converge. Even if it does grow exponentially, you can just pick ##\epsilon## close to 1. It feels like the output of this might be useful for certain types of analysis, I'm not sure.
 
Office_Shredder said:
A hacks thing you can do, is instead of writing 1, write ##1-\epsilon## in the adjacency matrix
Then each entry of ##A^n## is ##k(1-\epsilon)^n## where ##k## is the number of paths of length ##n##. As long as the number of paths doesn't grow exponentially in n this sum should converge. Even if it does grow exponentially, you can just pick ##\epsilon## close to 1. It feels like the output of this might be useful for certain types of analysis, I'm not sure.
I just tried that for K2. It produces
##(I-A)^{-1}=\frac 1{\epsilon(2-\epsilon)}\begin{pmatrix} 1&\epsilon-1\\\epsilon-1&1\end{pmatrix}##
The off diagonal terms are negative. Can’t think of any useful interpretation.
 

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