Constructing dimensions out of a graph structure?

In summary, the conversation discusses the possibility of constructing a graph that effectively simulates a 3 dimensional space. It is suggested that using a 3-D grid or spin networks may fulfill this requirement. However, it is also noted that using a grid may result in a taxicab metric instead of the desired Euclidean metric. The conversation also mentions the use of spin networks in loop quantum gravity to quantize area and volume.
  • #1
FlorianDietz
1
0
I have a question to anyone experienced with graphs and topology. The question is relevant for this topic: https://www.physicsforums.com/threads/a-graph-based-model-of-physics-without-dimensions.887694/

Is it possible to construct an arbitrarily large graph (a set of vertices, a set of edges), such that the following is true:

There exists a mapping f(v) of vertices to (x,y,z) coordinates such that
For any pair of vertices m,n:
the euclidean distance of f(m) and f(n) is approximately equal to the length of the shortest path between m and n (inaccuracies are fine so long as the distance is small, but the approximation should be good at larger distances).

In other words, is it possible to construct a graph that effectively simulates a 3 dimensional space?
 
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  • #3
FlorianDietz, this is an interesting question.

But just asking for a mapping *from* the vertices of some graph G *to* 3-dimensional Euclidean space R3 is not at all the same as asking whether it's possible to construct a graph "that effectively simulates a 3 dimensional space".

So I'm not sure if you are phrasing the question in such a way as to ask what you are really interested in.

For example, I bet you can find such a mapping if a) G has 1 vertex and 0 edges, or b) G has 2 vertices and 1 edge, or c) if G has 3 vertices and 3 edges arranged in a triangle, or d) if G has n+1 vertices and n edges arranged in a straight line . . . but no one would think these come close to "simulating" even a finite portion of 3-dimensional space.

Maybe if you thought about this question a little more you might rephrase the question so that it comes closer to what you are aiming at? One possibility is that you would like to find an *infinite* graph G with a mapping of its set V of vertices into R3:

f: V → R3

with certain properties that you might like to have? Or just let the vertices and edges of G be a subset of R3.

For instance, you could imagine the graph whose vertices correspond to all the points of R3 having integer coordinates (K, L, M), and whose edges are all the unit intervals connecting a vertex to its 6 nearest neighbors (right, left, front, back, up down).

In that case, in what precise sense would that graph "simulate" R3 ?
 
  • #4
FlorianDietz said:
In other words, is it possible to construct a graph that effectively simulates a 3 dimensional space?

A 3-D grid can be regarded as a graph. It seems to me that a fine enough grid satisfies your requirement.
 
  • #5
Stephen Tashi said:
A 3-D grid can be regarded as a graph. It seems to me that a fine enough grid satisfies your requirement.
That gives a taxicab metric instead of the Euclidean metric the OP wants.
 
  • #6
The Bill said:
That gives a taxicab metric instead of the Euclidean metric the OP wants.

Can we connect diagonally opposite vertices with an edge ?
 
  • #7

1. What is the purpose of constructing dimensions out of a graph structure?

The purpose of constructing dimensions out of a graph structure is to better understand the relationships and connections between different variables. By visualizing data in a graph, it becomes easier to identify patterns and trends, and to make more accurate predictions and decisions based on the data.

2. How do you determine the dimensions in a graph structure?

The dimensions in a graph structure are determined by the variables being plotted on the graph. Each axis represents a different variable, and the intersection of these variables creates the dimensions. For example, in a scatter plot, the x-axis represents one variable and the y-axis represents another, resulting in two dimensions.

3. Can constructing dimensions out of a graph structure be applied to any type of data?

Yes, constructing dimensions out of a graph structure can be applied to any type of data as long as it is represented in a numerical or categorical form. This includes data from various fields such as science, economics, social sciences, and more.

4. What are some common graph structures used for constructing dimensions?

Some common graph structures used for constructing dimensions include scatter plots, line graphs, bar graphs, and pie charts. These structures can be further customized and combined to create more complex and informative visualizations of data.

5. How can constructing dimensions out of a graph structure help with data analysis?

Constructing dimensions out of a graph structure can help with data analysis by providing a visual representation of the data, making it easier to identify patterns, outliers, and relationships between variables. This can aid in making more informed decisions and predictions based on the data.

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