Estimation for a Graph Theory problem

In summary, the conversation discusses a problem involving a graph with 20 nodes and 6 edges being randomly added at each instance of 4 nodes being picked. The question is how many iterations it would take for the graph to become connected. The estimated number of rounds lies within the interval of 5 to infinity, with a minimal chance of 3.84%. The chances increase with each round, but the exact probability is unknown.
  • #1
chingkui
181
2
I am thinking about this problem that come up in some of my work, I think this has been solved before, though I am not aware where I could find the solution. Here is the question:
Suppose a graph has say 20 nodes with no edge initially, and at each instance, 4 (different) nodes are randomly drawn with equal probability and all 6 edges among them are added to the graph. How do I estimate the expected number of iterations before the graph become connected (just connected, no need to be completed)? Thanks.
 
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  • #2
I feel that I should include I am not an expert by any means, but here is how I would approach it.

So all you really need is the 20 nodes and 4 picked each time right, I think the graph will be connected once every node is picked once regardless of the extra 2 edges they don't matter, I don't know the answer, but I think the question could be asked "Picking 4 at a time, how long does it take for all 20 to be picked at random?" I guess one answer is that the number of rounds lies in the interval [5,∞).
The chance goes up with every round, with a minimal chance of 5!/(5^5) = 3.84%

for the 5 rounds to work, you have to get unpicked nodes every round and you'd have a first round with a 20/20 chance of getting an unpicked node, second round, 16/20 chance of getting an unpicked node,3rd 12/20 and so on, which reduces to 5/5,4/5,3/5,2/5,1/5, from which you get the above. How you would figure the chances if it didn't go perfect I am not sure of, sorry.
 
  • #3
And actually that is bad, each round would break into the 4 individual draws? So you'd have 1*16/20*15/19*14/18*13/17*12/20*11/19...

so: [itex] \frac{20!}{(20^5)(19^5)(18^5)(17^5)} =\frac{20!}{(\frac{20!}{16!})^5} = .000000114 = .0000114% [/itex]% chance as a minimum?
 
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FAQ: Estimation for a Graph Theory problem

1. What is the purpose of estimation in graph theory?

Estimation in graph theory is used to make predictions or approximations about various aspects of a graph, such as its size, complexity, or behavior. It helps to provide insight into the properties of a graph and can aid in solving complex problems.

2. How do you estimate the size of a graph?

The size of a graph can be estimated by counting the number of vertices and edges in the graph. This can be done manually for small graphs, or by using mathematical formulas for larger graphs. Additionally, sampling techniques can be used to estimate the size of very large graphs.

3. Can estimation be used to determine the shortest path in a graph?

Yes, estimation techniques can be used to approximate the shortest path in a graph. This can be done by using algorithms such as Dijkstra's algorithm, which uses estimation to find the shortest path between two vertices in a graph.

4. How accurate are estimation techniques in graph theory?

The accuracy of estimation techniques in graph theory depends on various factors, such as the size and complexity of the graph, the chosen estimation method, and the quality of data. In general, estimation techniques provide good approximations but may not always be 100% accurate.

5. Are there any limitations to using estimation in graph theory?

Yes, there are some limitations to using estimation in graph theory. Estimation techniques may not work well for extremely large or complex graphs, and they may not be able to account for all possible scenarios. Additionally, the accuracy of estimations may be affected by errors or inconsistencies in the data.

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