Proving Stable Set Size in Graphs with Average Degree d

In summary, the conversation discusses using a probabilistic method to prove that a graph with an average degree of d will have a stable set of at least size n/(2d). The idea is to calculate the expected number of stable sets and show that it exceeds the bound of n/2d. However, the speaker is not confident in their understanding of probabilistic graph theory and suggests seeking further guidance.
  • #1
Dragonfall
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Homework Statement



Show using a probabilistic method that a graph with average degree d has a stable set of cardinality at least n/(2d).

I can't think of a probabilistic method that will do this.
 
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  • #2
What is a stable set?

Work out the expected number of stable sets, I suppose - if all stable sets were of size less than n/2d, then that might be a bound on the expectation - show the bound is exceeded.

But that is just a guess as to the style of how probabilistic graph theory works - I've never answered a question on it and read precisely on paragraph about it once, so don't trust me.
 

1. What is the concept of stable set size in graphs?

The stable set size in a graph refers to the maximum number of vertices in the graph that do not share any edges with each other. In other words, it is the size of the largest subset of vertices that do not form a complete subgraph.

2. How is the average degree of a graph related to its stable set size?

The average degree of a graph, denoted by d, is the average number of edges incident to each vertex in the graph. It has been proven that the stable set size in a graph is at most floor(n/(d+1)), where n is the total number of vertices in the graph.

3. What is the importance of proving stable set size in graphs?

Understanding the stable set size in a graph can provide valuable insights into the structure and properties of the graph. It can also help in solving various optimization problems related to graph theory.

4. What are some techniques used to prove stable set size in graphs with average degree d?

There are various techniques used to prove stable set size in graphs, including linear programming, semidefinite programming, and combinatorial optimization. These techniques involve formulating the problem as a mathematical optimization problem and finding the optimal solution.

5. Are there any real-world applications of proving stable set size in graphs?

Yes, the concept of stable set size in graphs has various real-world applications, such as in computer science, social networks, and logistics. For example, in computer science, it can be used to optimize data storage by finding the maximum number of non-overlapping tasks that can be executed simultaneously. In social networks, it can help in identifying groups of people with no connections to each other. In logistics, it can assist in optimizing transportation routes by finding the maximum number of locations that can be visited without any overlap.

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