Probability of Having a Totally Dominating Set (Probabilistic Methods)

In summary: Thus, $P[S$ is totally t-dominating$] > \frac{1}{2}$.In summary, using Chernoff's inequality, we can prove that the probability of a random vertex set $S$ being totally t-dominating in an r-regular n-vertex graph $G$ with specific values of r, t, and n is greater than $\frac{1}{2}$.
  • #1
joypav
151
0
Problem: A vertex set $S$ in a graph $G$ is said to be totally t-dominating, for a positive integer t, if
$|N(v) \cap S| \geq t$ for all $v \in V (G)$.
Suppose that r, t, n are positive integers such that $r > 2t$ and $t \geq \frac{14}{3}\cdot ln(2n)$, and let $G$ be an r-regular n-vertex graph. Choose a random vertex set $S$ by independently adding each vertex to $S$ with probability p, where $p =\frac{2t}{r}$. Prove that $P[S$ is totally t-dominating$] > \frac{1}{2}$.

I honestly am not sure where to begin with this problem.
I know that $E[|S|] = n \cdot p = n \cdot \frac{2t}{r}$ (the expectation of the size of $S$) and $|E(G)| = \frac{nr}{2}$ (because G is r-regular). I don't know if either of these will be of any use...

Here is what I was thinking.
Fix $v \in V(G)$. $G$ is r-regular, so label each of $v$'s neighbors: $N(v) = \left\{u_1,...,u_r\right\}$.
Let $A_{v,u_i}$ be the event that $u_i \in S$.
Let $X_{v,u_i}$ be the indicator for $A_{v,u_i}$, (i.e. it is equal to 1 if $u_i \in S$ and 0 otherwise).
Let $X_v$= the # of neighbors of $v$ that are in $S$.
Then, $X_v = \sum_{u_i \in N(v)} X_{v,u_i}$.

We "want" $v$ to have at least t neighbors in $S$. So then we'd like to look at $P[X_v \geq t]$.

Then, consider the event $X$: $S$ is totally t-dominating.
We want to show that $P[X] > \frac{1}{2}$.

Is this at all on the right track?
 
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  • #2
Another proof for your consideration (I was busy busy busy working on problems today!).

Proof:
Fix $v \in V(G)$. G is r-regular, so we know $|N(v)|=r$. Label $v$'s neighbors $u_1, ..., u_r$. Let $A_{u_i}$ be the event that $u_i \in S$ and let $X_{u_i}$ be its indicator. Let $X_v = \sum_{i=1}^r X_{u_i}$, (giving the number of neighbors that $v$ has in $S$). Using Chernoff's, we may bound the "bad" event: that $v$ has less than $t$ neighbors in $S$. Apply Chernoff's with,
$$E[X_v] = \sum_{i=1}^r X_{u_i} = r(p) = r(\frac{2t}{r})=2t$$
$$P[|N(v) \cap S| \leq t] = P[X_v \leq t] = P[X_v \leq 2t - t] = P[X_v \leq E[X_v] - t]$$
$$\leq e^{-\frac{t^2}{2(np+t/3)}} = e^{-\frac{t^2}{2(4tn/r+2t/3)}} = e^{-\frac{t^2}{\frac{12tn+2tr}{3r}}} = e^{-\frac{3rt^2}{12tn+2tr}} = e^{-\frac{3rt}{12n+2r}} < e^{-\frac{3(14/3ln(2n)}{14}} = e^{-ln(2n)}$$
Then,
$$\sum_{v \in V(G)} P[|N(v) \cap S| \leq t] = \sum_{v \in V(G)} P[X_v \leq t] < n \cdot e^{-ln(2n)} = e^{ln(n)-ln(2n)} = e^{ln(n/2n)} = e^{ln(1/1)} = \frac{1}{2}$$
If the probability of the "bad" event is less than $\frac{1}{2}$, then the probability of $S$ being totally t-dominating $> 1-\frac{1}{2} = \frac{1}{2}$.
 

FAQ: Probability of Having a Totally Dominating Set (Probabilistic Methods)

What is a totally dominating set?

A totally dominating set in a graph is a subset of vertices that can reach every other vertex in the graph by at least one edge. In other words, every vertex in the graph is either in the set or adjacent to a vertex in the set.

How is probability used in determining the existence of a totally dominating set?

Probability is used in probabilistic methods to estimate the likelihood of a totally dominating set existing in a given graph. This is done by randomly selecting subsets of vertices and checking if they form a totally dominating set. The more subsets that are checked, the higher the probability of finding a totally dominating set.

Can probabilistic methods guarantee the existence of a totally dominating set?

No, probabilistic methods cannot guarantee the existence of a totally dominating set. They can only provide a probability of its existence. However, the higher the probability, the more likely it is that a totally dominating set exists in the graph.

How does the size of the graph affect the probability of having a totally dominating set?

The size of the graph can greatly affect the probability of having a totally dominating set. Generally, as the size of the graph increases, the probability of having a totally dominating set decreases. This is because larger graphs have more vertices and edges, making it less likely for a randomly selected subset to form a totally dominating set.

Are there other methods besides probabilistic methods for determining the existence of a totally dominating set?

Yes, there are other methods such as brute force algorithms and heuristic algorithms that can be used to determine the existence of a totally dominating set. However, these methods may be computationally expensive and may not be feasible for larger graphs. Probabilistic methods provide a more efficient and scalable approach for estimating the existence of a totally dominating set.

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