Boolean squaring of adjacency matrix

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In summary, the matrix ##M^2## is the adjacency matrix of the graph of some relation, and it has entries which specify the number of paths of length 2.
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s3a
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Homework Statement


I have come across the following statement which I've written for myself a long time ago, and I don't remember what is going on.:
The adjacency matrix of R ◦ R is ##M^{(2)}##, where (2) signifies the square using boolean algebra.

Homework Equations


Boolean Squaring.

The Attempt at a Solution


Sorry for the potentially stupid question, but I've been researching how to compute ##M^{(2)}##, and the best I found was how to get an adjacency matrix from a graph, and computing ##M^2## (instead of ##M^{(2)}##).

If someone could either explain to me the ##M^{(2)}## part or point me to something that explains that, I would very much appreciate it!
 
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  • #2
"Boolean algebra" is essentially algebra on "true", "false" or, equivalently "1", "0" with the addition rules that 0+ 0= 0, 1+ 0= 0+ 1= 1, and 1+ 1= 0 and multiplication rules 0*0= 0*1= 1*0= 0 and 1*1= 1.
 
  • #3
For 1 + 1, I think you meant 1 + 1 = 1, but that's not my problem; my problem is the parentheses around the 2 . . . what's the difference between a power within parentheses and a power that's not within parentheses (in this context)?
 
  • #4
s3a said:
For 1 + 1, I think you meant 1 + 1 = 1, but that's not my problem; my problem is the parentheses around the 2 . . . what's the difference between a power within parentheses and a power that's not within parentheses (in this context)?

The matrix ##M^k## has entries which specify the number of paths of length k; that is, the element ##M^k(i,j)## is the number of length-k paths from i to j. The matrix ##M^{(k)}## just says whether there are length-k paths; that is, ##M^{(k)}(i,j)## is 0 if there are no length-k paths from i to j and is equal to 1 if there is at least one length-k path from i to j.
 
  • #5
So does it not make sense to ask:

Given that the matrix M is the adjacency matrix of the graph of some relation R.

What is the adjacency matrix of R◦R?"

to which the answer is

The adjacency matrix of R◦R is ##M^{(2)}##, where (2) signifies the square using boolean algebra.
?
 
  • #6
Edit: I accidentally double-posted.
 
  • #7
Actually, Ray Vickson, what you said gave me the ammunition to better understand what someone else explained to me.

Thank you both.
 

1. What is Boolean squaring of adjacency matrix?

Boolean squaring of adjacency matrix is a mathematical operation used to represent the relationships between elements in a network or graph. It involves taking the adjacency matrix, which represents the connections between nodes in a graph, and squaring it using Boolean logic. This operation results in a new matrix that represents the existence of a path between any two nodes in the original graph.

2. How is Boolean squaring of adjacency matrix used in scientific research?

Boolean squaring of adjacency matrix is commonly used in fields such as computer science, physics, and biology to model and analyze complex networks. It allows researchers to identify patterns and relationships within the network, which can provide insights into the dynamics and behavior of the system being studied.

3. What are the benefits of using Boolean squaring of adjacency matrix compared to other methods of network analysis?

Boolean squaring of adjacency matrix is a relatively simple and efficient method for analyzing networks. It can handle large and complex networks, and it allows for the identification of important nodes and pathways within the network. Additionally, it can be easily modified to incorporate other factors, such as weights or directionality, making it a versatile tool for network analysis.

4. Are there any limitations to using Boolean squaring of adjacency matrix?

While Boolean squaring of adjacency matrix is a useful tool for network analysis, it does have some limitations. It assumes that all connections between nodes are equally important, and it does not account for the strength or directionality of these connections. Additionally, it may not be suitable for networks with overlapping or hierarchical structures.

5. What are some real-world applications of Boolean squaring of adjacency matrix?

Boolean squaring of adjacency matrix has been used in a variety of real-world applications, including social network analysis, identifying key players in drug interactions, and analyzing gene regulatory networks. It has also been used to study the spread of infectious diseases and to model interactions between species in ecological networks.

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