Rambling on Graphs

# Sparsity of minor-free graphs

Planar graphs are sparse: any planar graph with $n$ vertices has at most $3n-6$ edges. A simple corollary of this sparsity is that planar graphs are $6$-colorable. There is simple and beautiful proof based on the Euler formula, which can easily be exteded to bounded genus graphs, a more general case: any graph embedddable in orientable surfaces of genus $g$ with $n$ vertices has at most $3n + 6g-6$ edges.

How’s about the number of edges of $K_r$-minor-free graphs? This is a very challenging question. A reasonable speculation is $O(r)\cdot n$: a disjoint union of $n/(r-1)$ copies of $K_{r-1}$ excludes a $K_r$ minor and has $\Theta(r)\cdot n$ edges. But this isn’t the case. And surprisingly, the correct bound is $O(r\sqrt{\log r})n$, which will be the topic of this post.

Theorem 1: Any $K_r$-minor-free graphs with $n$ vertices has at most $O(r\sqrt{\log r})\cdot n$ edges.

The bound in Theorem 1 is tight; see a lower bound in Section 4. The sparsity bound, which is the ratio of the number of edges to the number of vertices, $O(r\sqrt{\log r})$ was first discovered by Kostochka ; the proof is quite non-trivial, so as other follow-up proofs. A short proof was just found recently by Alon, Krivelevich, and Sudakov (AKS) , which I will present here in Section 3. See the bibliographical notes section for a detailed discussion of other proofs.

The goal of this post isn’t just to present the proof of Theorem 1. At various points in the past, I am interested in a more intuitive proof that gives good enough sparsity bounds, say $O(\mathrm{poly}(r))$, or even $O(f(r))$ bound for any function that depends on $r$ only. This post describes different proofs, of increasing complexity (or ingenuity), giving different bounds. In particular, sparsity bound $2^{r}$ follows by a simple induction. Sparsity bound $O(r^2)$ relies crucially on the fact that a graph of sparsity $d$ has a highly connected minor of small size; the high connectivity allows us to show that for any vertex subset of size $k \approx \sqrt{d}$ has ${k \choose 2}$ internally vertex-disjoint paths connecting these vertices, thereby giving us a $K_{\sqrt{d}}$ minor. The optimlal bound $O(r\sqrt{\log r})$ also relies on a highly connected minor of small size, but employs a clever probabilistic argument to construct a $K_{r}$ minor.

# 1. Exponential Sparsity: $2^{r}$

I learn a beatiful proof of the following theorem in the graph theory book of Reinhard Diestel (Proposition 7.2.1. ).

Theorem 2: Any $K_r$-minor-free graphs with $n$ vertices has at most $2^{r-1}\cdot n$ edges.

Proof: Let $G$ be a $K_r$-minor-free graph with $n$ vertices. The proof is by induction on $\vert V(G) \vert + r$. If there is a vertex $v$ of degree at most $2^{r-1}$, then by removing $v$ and applying the induction hypothesis, we are done. Now consider the case where every vertex has degree more than $2^{r-1}$; let $v$ be such a vertex. The key idea is to find an edge $(u,v)$ such that $u$ and $v$ share only a few neighbors. We then contract $(u,v)$ and apply induction.

Claim 1: There is a neighbor $u$ of $v$ such that $\vert N_G(v)\cap N_G(u) \vert \leq 2^{r-1}-1$.

Suppose that the claim holds, then let $G’$ be the graph obtained from $G$ by contracting $(u,v)$, i.e., $G’= G/(u,v)$. Then $\vert V(G’) \vert \leq r-1$ and $\vert E(G’) \vert \geq \vert E(G) \vert - 2^{r-1}$. By induction, $\vert E(G’) \vert \leq 2^{r-1}(n-1)$, which implies $\vert E(G) \vert \leq 2^{r-1}(n-1) + 2^{r-1} = 2^{r-1}\cdot n$ as desired.

We now turn to Claim 1. Let $H = G[N_G(v)]$ be the sugraph induced by $N_G(v)$. Then $H$ is $K_{r-1}$-minor-free. By induction, $\vert E(H) \vert \leq 2^{r-2}\cdot \vert V(H) \vert$. Thus, there exists a vertex $u\in H$ such that $d_H(u) \leq 2 \vert E(H) \vert / \vert V(H) \vert \leq 2^{r-1}$. This gives $\vert N_G(v)\cap N_G(u) \vert \leq d_H(u)-1 = 2^{r-1}-1$.

The exponential term $2^{r-1}$ in the above theorem is due to a loss of a factor of $2$ in each step of the induction by using $d_H(v) \leq 2( \vert E(H) \vert / \vert V(H) \vert )$.

# 2. Polynomial Sparsity: $O(r^2)$

The goal of this section is to improve the exponential bound in Theorem 2 to a polynomial bound.

Theorem 3: Any $K_r$-minor-free graphs with $n$ vertices has at most $O(r^2)\cdot n$ edges.

Proving Theorem 3 requires a deeper understanding of the structures of minor-free graphs. The key insight is that any graph with at least $d\cdot n$ edges has a minor, say $K$, of $O(d)$ size that is highly connected (Lemmas 1 and 2 below). Then we then show that given any set of vertices, say $R$, of size about $\Theta(\sqrt{d})$ in $K$, we can find a collection of pairwise disjoint paths of $H$ connecting every pair of vertices in $R$ (due to high connectivity), which gives us a clique minor of size $\Theta(\sqrt{d})$ (Lemma 3). Taking contrapositive gives Theorem 3.

Let $\varepsilon(G) = \vert E(G) \vert / \vert V(G) \vert$ and $\delta(G)$ be the minimum degree of $G$. Fist, we show that $G$ contains a minor of size $\leq 2d$ and minimum degree at least $d$.

Lemma 1: Let $G$ be any graph of $n$ vertices such that $\vert E(G) \vert \geq d\cdot n$. Then $G$ has a minor $H$ such that $\delta(H)\geq d \geq \vert V(H) \vert /2$.

Proof: Let $K$ be a minimal minor of $G$ such that $\varepsilon(K)\geq d$. The minimality implies two properties:

1. $\vert N_K(u)\cap N_K(v) \vert \geq d$ for every edge $(u,v)$; otherwise, we can contract edge $(u,v)$.
2. There exists a vertex $x$ such that $d_K(x)\leq 2d$; otherwise, we can delete an edge from $K$.

Then $H = K[N_K(x)]$ satisfying the lemma.

Next, we show that $H$ has a highly connected subgraph.

Lemma 2: Let $H$ be such that $\delta(H)\geq d \geq \vert V(H) \vert /2$. Then $H$ has a subgraph $K$ that has (i) $\vert V(K) \vert \leq 2d$ and (ii) $K$ is $d/3$-vertex connected.

Proof: If $H$ is $d/3$-vertex connected, then $K = H$. Otherwise, there is a set $S\subseteq V(H)$ of size at most $d/3$ such that $H\setminus S$ has at least two connected components. Let $K$ be the smallest connected component of $H$. Then $\vert V(K) \vert \leq \vert V(H) \vert /2\leq d$ and $\delta(K)\geq \delta(H) - \vert S \vert \geq 2d/3$. Thus, for every $u\not= v\in K$, $u$ and $v$ must share at least $\vert N_K(u) \vert + \vert N_K(v) \vert - \vert V(K) \vert \geq d/3$ neighbors in $K$. That is, $K$ is $d/3$-vertex connected.

A detour: diameter of highly connected graphs. Graph $K$ in Lemma 2 has another nice property: its diameter is at most $7$. To see this, suppose that there is a shortest path ${v_0,v_1,\ldots,v_8, \ldots}$ of length at least $8$. Consider three vertices $v_1,v_4, v_7$. Then $N_{K}(v_1),N_{K}(v_4), N_{K}(v_7)$ are pairwise disjoint. As $K$ is $d/3$-connected, $\delta(K)\geq 3$. Thus, $\vert N_{K}(v_1)\cup N_{K}(v_4) \cup N_{K}(v_7) \vert \geq 3(d/3+1) > d\geq V(K)$, a contradiction. We will use the same kind of arguments in several proofs below.

We now construct a minor of size $\Theta(\sqrt{d})$ for graph $K$ in Lemma 2. We do so by showing that for any given $p \leq d/40$ distinct pairs of vertices ${(s_1,t_1, \ldots, (s_p,t_p)}$ in $K$ (two pairs might share the same vertex), then there are $p$ internally vertex-disjoint paths connecting them (Lemma 3). (Two paths are internally vertex-disjoint if they can only share endpoints.) Then one can construct a minor of size $\sqrt{d/40}$ by picking an arbitrary set $R$ of $\sqrt{d/40}$ vertices, and connect all pairs of vertices in $R$ using disjoint paths in Lemma 3, which implies Theorem 3. Figure 1: (a) $\mathcal{P}$ includes two paths of black edges. (b) deleting $\mathcal{P}$ except $s_1,t_1$. (c) $v$ could not have more than 3 neighbors on the path from $s_i$ to $t_i$

Lemma 3: Let $\mathcal{T} = {(s_1,t_1), \ldots, (s_p,t_p)}$ be any $p \leq d/40$ distinct pairs of vertices in $K$ (in Lemma 2). Then there are $p$ internally vertex-disjoint paths connecting the all pairs in $\mathcal{T}$.

Proof: Let $\mathcal{P}$ be a set of internally vertex-disjoint paths, each of of length at most $10$, that connects a maximal number of pairs in $\mathcal{T}$. Subject to the pairs connected by $\mathcal{P}$, we choose $\mathcal{P}$ such that the total number of edges of paths in $\mathcal{P}$ is minimal. If $\mathcal{P}$ connects every pair, we are done. Otherwise, w.l.o.g, we assume that $s_1$ and $t_1$ are not connected by $\mathcal{P}$. See Figure 1(a).

Let $K^-$ be obtained from $K$ be removing all vertices in $\mathcal{P}$, except $s_1$ and $t_1$, from $K$. See Figure 1(b). Observe that the total number of vertices in $\mathcal{P}$ is at most $11\cdot p = 11d/40 < d/3$. Since $K$ is $d/3$-vertex-connected, $K^-$ is connected.

Claim 2: for every $v\in K^-$ and $P\in \mathcal{P}$, $\vert N_K(v)\cap V(P) \vert \leq 3$.

Suppose the claim is not true, then we can shortcut $P$ via $v$ to get a shorter path connecting the endpoints of $P$, contradicting the minimality of $\mathcal{P}$. See Figure 1(c).

Note that $\delta(K)\geq d/3$ as it is $d/3$-connected. By Claim 2, $\delta(K^-)\geq \delta(K) -3\cdot p \geq d/3 - 3d/30 \geq d/4$. The same argument in the detour above implies that $K^-$ has diameter at most $10$. Thus, there is a path of length at most $10$ from $s_1$ to $t_1$ in $K^-$, contradicting the maximality of $\mathcal{P}$.

# 3. Optimal Sparsity: $O(r\sqrt{\log{r}})$

We assume that the graph has at least $d\cdot n$ edges. Our goal is to construct a minor of size $\Omega(d/\sqrt{\log d})$. By taking contrapositive, we obtain Theorem 1.

## 3.1. Proof Ideas

By Lemma 2, it suffices to construct a clique minor of size $\Omega(d/\sqrt{\log d})$ for a $d/3$-vertex-connected graph $K$ with at most $2d$ vertices. In particular, we will construct a collection of $h = d/(c_1 \sqrt{\log d})$ vertex-disjoint connected subgraphs $C_1,C_2,\ldots, C_h$ such that (a) there is an edge between any two subgraphs $C_i,C_j$ for $1\leq i\not=j \leq h$ and (b) each $C_i$ has $O(c_0) \sqrt{\log d}$ vertices, for some constant $1\ll c_0 \ll c_1$. These subgraphs will realize a $K_h$-minor of $K$.

The choices of constants $c_0$ and $c_1$ imply that $\vert V(C_1)\cup \ldots \cup V(C_h) \vert \leq d/12$. Thus, if we let $H_i = K\setminus (C_1\cup \ldots \cup C_i)$ for any $i\leq h$, then $H_i$ is $d/3 - d/12 = d/4$ vertex-connected. This in particular, implies that $H_i$ has diameter at most $22 = O(1)$. Figure 2: (a) $C_1$ forms from $S_1$, the set of black vertices, and its bad set $B_1$. (b) $C_2$ is constructed from $S_2$ (black vertices), which avoids $B-1$, and its bad set $B_2$. (c) $S_i$ has edges to all graphs $C_1,C_2,\ldots,C_{i-1}$.

We will construct each $C_i$ by random sampling. To gain intuition, let’s look at the first step: (1) sampling a set $S_1$ of $s = c_0\sqrt{\log n}$ vertices and (2) making $S_1$ connected by adding a shortest path from one vertex to every other vertex in $S_1$. See Figure 2 (a). There are two good reasons for doing this:

1. As the graph has diameter $O(1)$, $\vert V(C_1) \vert = O(c_0 \sqrt{\log d})$.
2. About $e^{-O(c_0)\sqrt{\log d}}\cdot d$ vertices are not dominated by $S_1$. This is because each vertex has at least $d/3$ neighbors (as $K$ is $d/3$-connected), and hence probability that a vertex is not dominated by $S_1$ is at most $(1-d/(3\cdot 2d))^{s} = e^{-O(c_0)\sqrt{\log d}}$. Let’s call these vertices bad vertices (for a reason explained later), and denote the set of bad vertices by $B_1$. (A vertex is donimated by $S_1$ if it is in $S_1$ or adajcent to another vertex in $S_1$.)

The second step, we sample $S_2$ in exactly the same way and make $C_2$ by adding paths between vertices of $S_2$. The graph we construct $C_2$ now is $H_1 = K\setminus V(C_1)$, and as argue above, $H_1$ has roughly the same properties of $K$: $d/4$-vertex-connected and diameter $O(1)$. We want $S_2$ to contain a vertex adjacent to $S_1$ (in $K$) because we would like $C_2$ to be adjacent to $C_1$. That is, we want $S_2 \not\subseteq B_1$: we say that $S_2$ avoids bad set $B_1$. See Figure 2(b). The reason 2 above implies that $\mathrm{Pr}[S_2\subseteq B_1] \leq (e^{-O(c_0)\sqrt{\log d}})^{c_0\sqrt{\log d}} \leq 1/d^2$ for some chocie of $c_0\gg 1$. Thus, w.h.p, $S_2$ avoids $B_1$.

In general, at any step $i \in [1,h]$, we already constructed a set of $i-1$ vertex-disjoint conected subgraphs $C_1,C_2,\ldots C_{i-1}$, each is associated with a bad set (a set of non-neighbors). See Figure 2(c). We want to construct $C_i$ by sampling a set $S_i$ and adding paths between vertices of $S_i$. By the same reasoning above for $S_2$ and using the union bound, the probability that $S_i$ is connected to all $i-1$ subgraphs, i.e, $S_i$ avoids all the $(i-1)$ bad sets, is at least $1 - d/d^2 = 1-1/d > 0$. When $i = h$, we obtain a $K_h$ minor as desired.

## 3.2. The formal proof

Notation: for a given set $S\subseteq V$ in a graph $G = (V,E)$, denoted by $B_G(S)$ be the set of vertices not dominated by $S$. That is, $B_G(S) = V\setminus (S\cup(\cup_{v\in S}N_G(v)))$.

We construct a set of subgraphs $C_1,C_2,\ldots, C_h$ realizing a $K_h$-minor of $K$, for $h = d/(c_1 \sqrt{\log d})$, in $h$ iterations as follows.

Initially, $H_0 = K, B_0 = \emptyset$.

In $i$-th iteration, $i\geq 1$, we find a set $S_i$ of at most $c_0\sqrt{\log d}$ vertices s.t (a) $\vert B_{H_{i-1}}(S_i) \vert \leq 2de^{-c_0\sqrt{\log d}/8}$ and (b) $S_i$ is connected to each of $C_1,C_2,\ldots,C_{i-1}$ by an edge. Next, let $C_i$ be obtained by adding shortest paths from an arbitrary vertex $v\in S_i$ to every other vertex in $S_i\setminus {v}$, and $B_i= B_{H_{i-1}}(S_i)$. Then, we define $H_i = H_{i-1}\setminus V(C_i)$ for the next iteration.

Finally, output $C_1,C_2,\ldots, C_h$.

To show the correctness of the algorithm, we only have to show that the set $S_i$ at iteration $i$ exists, for some choices of $1\ll c_0 \ll c_1$. If so, $C_1,C_2,\ldots, C_h$ form a $K_h$-minor of $K$, and hence, of $G$.

First, we show that $H_i$ has high connectivity and $C_i$ has size $O(c_0\sqrt{\log d})$.

Lemma 4: For every $i\geq 1$, $\vert V(C_i) \vert \leq 22 c_0\sqrt{\log d}$ and $H_i$ is $d/4$-vertex connected when $c_1 = 12c_0$.

Proof: We prove by induction. Since $H_{i-1}$ is $d/4$-connected and $\vert V(H_i) \vert \leq 2d$, the diameter of $H_{i-1}$ is at most $22$. As we add at most $c_0\sqrt{\log d}$ shortest paths to $S_i$, $\vert V(C_i) \vert \leq 22 c_0\sqrt{\log d}$.

Observe that $\sum_{j=1}^{i} \vert V(C_j) \vert \leq c_0\sqrt{\log d}\cdot h= c_0\sqrt{\log d} \cdot d/(c_1\sqrt{\log d}) = d/12$. Since $K$ is $d/3$-vertex connected, $H_i$ is $d/3-d/12 = d/4$ vertex connected.

Now we show the existence of $S_i$. Note that condition (b) is equivalent to that $S_i$ avoids all the bad sets $B_0,B_1,\ldots, B_{i-1}$. Let $S_i$ be otabined by choosing each vertex of $H_{i-1}$ with probability $c_0\sqrt{\log d}/(2d)$; the expected size of $S_i$ is a most $c_0\sqrt{\log d}$. By Lemma 4, every vertex $v\in H_{i-1}$ has degree at least $d/4$. Thus, $\mathrm{Pr}[v\in B_{i}]\leq (1-c_0\sqrt{\log d}/(2d))^{d/4}\sim e^{-c_0\sqrt{\log d}/8}$. In particular, $\vert \mathbb{E}[B_i] \vert \leq (2d)e^{-c_0\sqrt{\log d}/8}$.

It remains to show that with non-zero probability, $S_i$ avoids all $B_0,\ldots, B_{i-1}$. Note that $\vert V(H_{i-1}) \vert \geq d/4$. For a fixed $j\in [0,i-1]$:

$\mathrm{Pr}[S_i\subseteq B_j]\leq ( \vert B_j \vert / \vert V(H_{i-1}) \vert )^{ \vert S_j \vert }\leq 8(e^{-c_0\sqrt{\log d}/8})^{c_0\sqrt{\log d}}= 8e^{-c_0^2 \log(d)/8} \leq 1/d^2$

for a sufficiently large $c_0\geq 1$.

By union bound, the probability that $\mathrm{Pr}[S_i\subseteq B_j]$ for some $j\in [0,i-1]$ is at most $h/d^2\leq 1/d$. Thus, the probability that $S_i$ avoids all $B_j$ is at least $1-1/d$. This conclude the proof.

# 4. A Lower Bound

In this section, we show that for any $n$ and $r$ such that $n \gg r\sqrt{\log r}$, there exists a graph $G$ with $n$ vertices and $\Theta(n\cdot r\sqrt{\log r})$ edges such that $G$ has no $K_{r}$ minor. The key idea of the construction is Theorem 4 below.

Theorem 4: There exists a graph $H$ with $k$ vertices and $\Theta(k^2)$ edges such that $H$ has no $K_s$-minor where $s = k/(\epsilon\sqrt{\log k})$ for some constant $\epsilon\in (0,1)$.

Theorem 4 implies a sparsity lower bound $\Omega(r\sqrt{\log r})$ as follows. Let $G$ be the disjoint union of $\Theta(n/(r\sqrt{\log r}))$ copies of the same graph in Theorem 4 with $k = \Theta(r\sqrt{\log r})$ vertices. Then $\vert E(G) \vert = \Theta(n/(r\sqrt{\log r}))k^2 = \Theta(n\cdot r\sqrt{\log r})$. As $H$ excludes a clique minor of size $k/(\epsilon\sqrt{\log k}) \leq r$ (by choosing the constant in the definition of $k$ appropriately), $G$ excludes $K_r$ as a minor.

Theorem 4 can be proven by the probabilistic method. To gain some intuition of the proof, consider any fixed partition of $V(H)$ into vertex-disjont subsets ${V_1,V_2,\ldots, V_{s}}$ of size $\epsilon \sqrt{\log k}$ each. For this partition to realize a $K_s$ minor, there must be an edge between every two vertex sets $V_i,V_j$ for $i\not=j$. The probability of this is:

$(1-2^{-|V_i||V_j|})^ = (1-2^{-\epsilon ^2 \log(k)})^ \approx e^{-k^{2 - \epsilon^2}/\log(k)}$

By the union bound over at most $k^k$ such partitions, the probability of having a $K_s$ minor is at most $k^k e^{-k^{2 - \epsilon^2}/\log(k)} \rightarrow 0$ when $k \rightarrow +\infty$. In other words, the probability of not having a $K_{s}$ minor is close to $1$.

In the formal proof, one has to work with the fact that the subsets in the partition might not have the same size; this can be resolved by simple algebraic manipulation.

Proof of Theorem 4. Let $H = G(k,1/2)$ where $G(k,1/2)$ is the Erdős–Rényi random graph with probability $p = 1/2$. We now show that the probability that $H$ contains a $K_s$ minor tends to $0$ when $k\rightarrow \infty$.

Recall that $K_s$ is a minor of $H$ if there is a set of non-empty, connected, and vertex-disjoint subgraphs $\mathcal{C} = {C_1,C_2,\ldots, C_s}$ such that there is an edge in $H$ connecting every two graphs $C_i,C_j$ for $1\leq i\not=j \leq s$.

We will bound the probability of exsiting such $\mathcal{C}$. Observe that the number of (ordered) partitions of $\vert V(H) \vert$ into $s$ non-empty subset is at most:

$\frac{k!}{s!}{k-1\choose s-1} <k^k$

Fixed such a partition of $\vert V(H) \vert$, denoted by $\mathcal{P}$. Let $n_i$ be the number of vertices in $i$-th set. The probability that there is an edge betwen two different sets $i$ and $j$ is $(1-2^{-n_i\cdot n_j})$. Thus, probability of having an edge between any two different sets of $\mathcal{P}$ is:

$\prod_{(i,j)}(1-2^{-n_i\cdot n_j}) \leq \prod_{(i,j)}e^{-2^{-n_i\cdot n_j}} = e^{-\sum_{(i,j)}2^{-n_i\cdot n_j}}$

where the product and sum is over all unordered pairs $(i,j)$. This implies that:

$\mathrm{Pr}[\mathcal{C} \text{ exists}] \leq k^k \cdot e^{-\sum_{(i,j)}2^{-n_i\cdot n_j}}$

We now estimate $\sum_{(i,j)}2^{-n_i\cdot n_j}$. By arithmetic–geometric mean inequality, $\sum_{(i,j)}2^{-n_i\cdot n_j}\geq {s \choose 2}\left(\prod_{(i,j)}2^{-n_i\cdot n_j}\right)^{1/{s\choose 2}} \geq {s \choose 2} \left(2^{-\sum_{(i,j)}n_i\cdot n_j}\right)^{1/{s\choose 2}}$

Observe that $\sum_{(i,j)}n_i\cdot n_j$ is the number of edges in a complete s-partite graph with $k$ vertices. Thus, $\sum_{(i,j)}n_i\cdot n_j\leq k^2/2$ and hence:

$\sum_{(i,j)}2^{-n_i\cdot n_j} \geq {s \choose 2} 2^{-k^2/s^2}$

Thus,

$\mathrm{Pr}[\mathcal{C} \text{ exists}] \leq k^k \cdot e^{- {s \choose 2} 2^{-k^2/s^2}}$

By chooosing $s = ck/(\sqrt{\log k})$ for some big enough constant $c$, we have $\mathrm{Pr}[\mathcal{C} \text{ exists}] \rightarrow 0$ when $k\rightarrow \infty$.

# Bibliographical Notes

The exponential sparsity bound in Section is due to Reinhard Diestel (Proposition 7.2.1. ). The $O(r^2)$ sparsity bound in Section 2 is obtained by a combination of various ideas, in particular, Lemma 1 is due to Exercise 21, Chapter 7, in , Lemma 2 is from the proof of Theorem 1 in , and Lemma 3 is a modification of Lemma 3.5.4 in .

Mader  proved a sparstiy bound $O(r\log(r))$ for $K_r$-minor-free graphs. Kostacha was the first to show that the sparsity is $\Theta(r\sqrt{\log r})$. Thomason  provided a more refined range for the sparsity bound: $[0.265r\sqrt{\log_2 r}(1+o(1)), 0.268r\sqrt{\log_2 r}(1+o(1))]$. The bound then was tightened exactly to $(\alpha +o(1))r\sqrt{\ln(r)}$ where $\alpha = 0.319…$ is an explcit constant, also by Thomason . The simpler proof presented in Section 3 is due to Alon, Krivelevich, and Sudakov .

The lower bound in Section 4 is due to Bollobás, Catlin, and Erdös .

 Alon, N., Krivelevich, M., and Sudakov, B. (2022). Complete minors and average degree–a short proof. ArXiv preprint arXiv:2202.08530.

 Bollobás, B., Catlin, P. A., and Erdös, P. (1980). Hadwiger’s conjecture is true for almost every graph. Eur. J. Comb., 1(3), 195-199.

 Diestel, R. (2017). Graph theory. Springer.

 Kostochka, A. V. (1982). A lower bound for the Hadwiger number of a graph as a function of the average degree of its vertices. Discret. Analyz. Novosibirsk, 38, 37-58.

 Mader, W. (1968). Homomorphiesätze für graphen. Mathematische Annalen, 178(2), 154-168.

 Thomason, A. (1984). An extremal function for contractions of graphs. In Mathematical Proceedings of the Cambridge Philosophical Society (Vol. 95, No. 2, pp. 261-265). Cambridge University Press.

 Thomason, A. (2001). The extremal function for complete minors. Journal of Combinatorial Theory, Series B, 81(2), 318-338.