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Write $\left(\genfrac{}{}{0ex}{}{[n]}{k}\right)$ for the $k$th uniform layer of the Boolean cube. For $\mathcal{F}\subseteq \left(\genfrac{}{}{0ex}{}{[n]}{k}\right)$, $1\le k\le n$, its lower shadow is $\partial \mathcal{F}=\{B:|B|=k-1,B\subset A\text{ for some }A\in \mathcal{F}\}$. The Local LYM inequality statesTo prove it, count pairs $(B,A)$ with $A\in \mathcal{F}$, $B\subset A$ and $|B|=k-1$. Each $A$ contributes $k$ pairs; each member of the lower shadow contributes at most $n-k+1$. Thus $k|\mathcal{F}|\le (n-k+1)|\partial \mathcal{F}|$, which is exactly the displayed Local LYM inequality. The corresponding upper shadow inequality follows either by taking complements or by counting upward inclusion pairs.

For an antichain $\mathcal{A}\subseteq \mathcal{P}([n])$, put ${\mathcal{A}}_k=\mathcal{A}\cap \left(\genfrac{}{}{0ex}{}{[n]}{k}\right)$. The LYM inequality isFor the first proof, set ${\mathcal{B}}_n={\mathcal{A}}_n$ and recursively ${\mathcal{B}}_k={\mathcal{A}}_k\cup \partial {\mathcal{B}}_{k+1}$. Every member of ${\mathcal{B}}_{k+1}$ is a subset of an original member of $\mathcal{A}$ of size at least $k+1$. Consequently ${\mathcal{A}}_k$ and $\partial {\mathcal{B}}_{k+1}$ are disjoint: otherwise one original member would properly contain another, contradicting the antichain condition. The Local LYM inequality now givesIterating down to $k=0$ bounds the desired sum by $|{\mathcal{B}}_0|\le 1$, proving the LYM inequality.

For the second proof, a permutation $(i_1,\dots ,i_n)$ determines a maximal chain in a Boolean lattice through the successive initial subsets $\varnothing ,\{i_1\},\dots ,[n]$. A member $A$ of size $k$ lies on exactly $k!(n-k)!$ of these $n!$ maximal chains in a Boolean lattice. An antichain meets each such chain in a partially ordered set at most once. Counting its incidences with the maximal chains in a Boolean lattice therefore yields ${\sum }_{A\in \mathcal{A}}|A|!(n-|A|)!\le n!$. Division by $n!$ is the LYM inequality again.

The Sperner theorem says that an antichain has at most $M=\left(\genfrac{}{}{0ex}{}{n}{\lfloor n/2\rfloor }\right)$ members. Indeed $\left(\genfrac{}{}{0ex}{}{n}{k}\right)\le M$ for every $k$, soThe middle uniform layer of the Boolean cube attains the bound. For equality in Sperner theorem, equality throughout forces every member to lie in a layer of maximum size. If $n=2k$, this forces $\mathcal{A}=\left(\genfrac{}{}{0ex}{}{[n]}{k}\right)$.

If $n=2k+1$, only ranks $k$ and $k+1$ are possible. Let $\mathcal{L}={\mathcal{A}}_k$, $\mathcal{U}={\mathcal{A}}_{k+1}$, and $\mathcal{S}=\nabla \mathcal{L}$ be the upper shadow. The antichain condition gives $\mathcal{U}\cap \mathcal{S}=\varnothing$. The Local LYM inequality for the two equally sized middle layers gives $|\mathcal{S}|\ge |\mathcal{L}|$, while $|\mathcal{L}|+|\mathcal{U}|=M$ forces $|\mathcal{S}|=|\mathcal{L}|$ and $\mathcal{U}=\left(\genfrac{}{}{0ex}{}{[n]}{k+1}\right)\setminus \mathcal{S}$.

Consider the inclusion bipartite graph between these two middle layers. It is a $(k+1)$-regular graph. Since $|\mathcal{S}|=|\mathcal{L}|$, all $(k+1)|\mathcal{S}|$ edges incident to $\mathcal{S}$ must come from $\mathcal{L}$; there are no edges from its complement on the lower side into $\mathcal{S}$. This bipartite graph is a connected graph: any two $k$-subsets can be joined by single-element exchanges, and each exchange passes through their common $(k+1)$-superset; every upper vertex has a lower neighbour. For $k=0$ it is just one edge. Hence either $\mathcal{L}=\varnothing$ or $\mathcal{L}$ is the whole lower layer. We have proved the complete classification:The two possibilities coincide for even $n$; for $n=0$ the unique maximum antichain is $\{\varnothing \}$.

Finally enumerate the members of the separating set system as $A_1,\dots ,A_m$ and encode each point $i\in [n]$ by $T_i=\{a\in [m]:i\in A_a\}$. Separation of the ordered pair $(i,j)$ means $T_i\setminus T_j\ne \varnothing$. Applying the hypothesis also to $(j,i)$ shows that the $n$ sets $T_i$ are distinct and pairwise incomparable. They form an antichain in $\mathcal{P}([m])$, so the Sperner theorem immediately gives

Identify the vertices of the Boolean hypercube $Q_n$ with subsets of $[n]$, adjacent when their symmetric difference has size one. Write $N(\mathcal{A})$ for the closed graph neighbourhood, including $\mathcal{A}$ itself. In simplicial order on the discrete cube, smaller sets come first, and equal-sized sets are ordered by lexicographic order: the least element of the symmetric difference belongs to the earlier member. Let $I_m$ denote the first $m$ vertices. The vertex-isoperimetric inequality in the discrete cube statesSubtracting $|\mathcal{A}|$ gives the equivalent assertion for the external vertex boundary.

We first check that $N(I_m)$ is also an initial segment in simplicial order on the discrete cube. Apart from the empty and full cases, $I_m$ consists of complete lower layers and a lexicographic initial segment $\mathcal{F}$ in its top rank $r$. Its closed graph neighbourhood contains all ranks at most $r$ and the upper shadow $\nabla \mathcal{F}$ in rank $r+1$. This upper shadow is lexicographic initial: an $(r+1)$-subset $C$ belongs to it precisely when its first $r$-subset in lexicographic order, obtained by deleting its largest element, belongs to $\mathcal{F}$. Taking the first $r$ elements preserves weak lexicographic order, proving the assertion. This also handles $r=0$, where $N(\{\varnothing \})$ consists of ranks zero and one.

We prove the vertex-isoperimetric inequality in the discrete cube by mathematical induction on $n$, with $n=0,1$ immediate. Fix a coordinate $i$ and write ${\mathcal{A}}_0,{\mathcal{A}}_1\subseteq Q_{n-1}$ for the sections without and with $i$. The two sections of its closed graph neighbourhood areA simplicial section compression replaces ${\mathcal{A}}_j$ by the initial segment $I_{a_j}$ of size $a_j=|{\mathcal{A}}_j|$ in the other coordinates. By the preceding observation, both $N(I_{a_j})$ and $I_{a_{1-j}}$ are initial segments, so their union has size $\max \{|N(I_{a_j})|,a_{1-j}\}$. The mathematical induction hypothesis givesAdding the two section bounds proves that simplicial section compression cannot increase the closed graph neighbourhood.

Apply changing simplicial section compressions until none remains. The sum of the global simplicial order on the discrete cube positions strictly decreases at each step: within a section the order is exactly the restriction of the global order. Thus the procedure terminates at a set family $\mathcal{B}$ every coordinate section of which is initial. We now classify the possible terminal families for simplicial section compression, rather than assuming this terminal set family must be globally initial.

If $X<Y$, $X\notin \mathcal{B}$ and $Y\in \mathcal{B}$, then $X,Y$ cannot agree in any coordinate, since that coordinate section would have an omitted earlier member and an included later one. Therefore $Y=X^c$. Let $X$ be the first omitted vertex and $Y$ the last included vertex. If $\mathcal{B}$ is noninitial, $X<Y$ and $Y=X^c$. Any vertex $Z$ strictly between them would have to be included, since an omitted $Z$ paired with $Y$ would have to equal $Y^c=X$. But it would also have to be omitted, since an included $Z$ paired with $X$ would have to equal $X^c=Y$. Thus there is no such $Z$: $X,Y$ are consecutive complementary vertices. Everything before $X$ is included and everything after $Y$ omitted. Hence $\mathcal{B}$ is obtained from the initial segment of its size by replacing $X$ with its immediate successor $X^c$.

There are only two forms of this exception. If $n=2r+1$, consecutive complementary vertices must straddle ranks $r,r+1$; otherwise an intermediate rank or a further vertex in one of their ranks separates them. They are consequentlywith $X$ the last $r$-subset and $Y$ the first $(r+1)$-subset. The corresponding initial segment is all ranks at most $r$. For $r\ge 1$, the omitted $X$ remains in $N(\mathcal{B})$ through a lower neighbour, and every $(r+1)$-subset has at least two $r$-subsets, only one of which was omitted. Thus $N(\mathcal{B})$ contains every rank at most $r+1$, namely $N(I_{|\mathcal{B}|})$. For $r=0$ both singleton closed graph neighbourhoods are the whole $Q_1$.

If $n=2r$, the two complementary vertices must both have rank $r$. The complement map reverses the lexicographic order in that rank, so a consecutive complementary pair must be its central pair. Exactly half the $r$-subsets contain $1$, and all of those precede those missing $1$. Therefore the pair isHere $I_{|\mathcal{B}|}$ comprises all ranks below $r$ and all $r$-subsets containing $1$. For $r\ge 2$, $N(\mathcal{B})$ contains every rank at most $r$, because all lower ranks were retained. Every $(r+1)$-subset containing $1$ has $r\ge 2$ different $r$-subsets containing $1$, so deletion of the one member $X$ removes no vertex of the upper shadow. Consequently $N(\mathcal{B})\supseteq N(I_{|\mathcal{B}|})$. For $r=1$, the two closed graph neighbourhoods both have size three in $Q_2$. These cases exhaust the exceptions. Since the successive simplicial section compressions never increased the closed graph neighbourhood, this proves the vertex-isoperimetric inequality in the discrete cube.

The same argument gives all integer radii. Write $N^t$ for $t$ repetitions of the closed graph neighbourhood. Its application to an initial segment is again initial; the sizes of these initial segments are monotone in the original size. Inducting on $t$ therefore gives

For the concentration question, use uniform probability measure on a finite connected graph and normalize its graph distance by its graph diameter. A sequence is a Lévy family of graphs if, for every fixed $\epsilon >0$ and $\delta >0$, the radius-$\epsilon$ neighbourhood of every set of measure at least $\delta$ has measure tending to one, uniformly over those sets. One may equivalently require this only for sets of measure at least one half. To see the reverse implication, first the radius-$\epsilon /2$ neighbourhood of every positive-density set must eventually have measure greater than one half. Otherwise its complement has measure at least one half, while the radius-$\epsilon /4$ neighbourhood of that complement misses the original positive-density set, contradicting the half-measure condition. Apply the half-measure condition once more to the radius-$\epsilon /2$ neighbourhood, enlarging it by another $\epsilon /2$, to obtain the full claim. The metric normalization is essential here.

For $Q_n$, the graph diameter is $n$. A uniformly chosen vertex has size $Z_n$ with binomial distribution $\mathrm{Bin}(n,1/2)$, expectation $n/2$ and variance $n/4$. Fix $\delta >0$ and choose $C>0$ so that $1/(4C^2)<\delta$. By Chebyshev's inequality,For sufficiently large $n$, put $k_n=\lfloor n/2-C\sqrt{n}\rfloor \ge 0$. Every initial segment of measure at least $\delta$ contains the complete ranks through $k_n$. Its radius-$t$ graph neighbourhood therefore contains every rank at most $k_n+t$. Taking $t=\lfloor \epsilon n\rfloor$, the all-radius vertex-isoperimetric inequality in the discrete cube gives, uniformly for $|\mathcal{A}|\ge \delta 2^n$,The last Chebyshev's inequality is used only when its denominator has positive square root. These are precisely the radius-$\epsilon$ neighbourhoods in the normalized graph distance. The discrete cubes form a Lévy family. In fact, taking $t=\lfloor n^{3/4}\rfloor$ in the same bound makes the omitted measure $O(n^{-1/2})$ for each fixed $\delta$: a radius that is $o(n)$ already suffices.

For fixed $d\ge 1$, the nearest-neighbour grid $[n{]}^d$ has graph distance ${\sum }_i|x_i-y_i|$ and graph diameter $d(n-1)$. Take the slab ${\mathcal{A}}_n=\{x:x_1\le \lceil n/2\rceil \}$, whose uniform measure is at least one half. Its integer radius-$t$ graph neighbourhood is exactly $\{x:x_1\le \min (n,\lceil n/2\rceil +t)\}$: changing the first coordinate alone attains the minimum distance to the slab. For $0<\epsilon <1/(2d)$ and $t=\lfloor \epsilon d(n-1)\rfloor$, the omitted proportion tends toThus there is failure of concentration in fixed-dimensional grids, and the fixed-dimensional grids do not form a Lévy family with normalized graph distance.

Let $C_x^b$ and $C_x^s$ be the out-clusters in directed percolation, with the site percolation cluster empty when $x$ is closed. Write ${\theta }_b(p)={\mathbb{P}}_p(|C_x^b|=\infty )$ and ${\chi }_b(p)={\mathbb{E}}_p|C_x^b|$, and similarly for site percolation. The survival and susceptibility thresholds for directed percolation are the infima of the parameters at which these quantities become positive and infinite, respectively.

We construct a coupling of probability distributions that compares the whole clusters. First condition the site percolation root to be open. Explore its out-cluster, using a fixed order of the finitely many outgoing bonds at each vertex. When a previously untested vertex $v$ is first encountered along a bond $u\to v$ from an already reached vertex, use a fresh Bernoulli random variable of parameter $p$ both for the state of $v$ and for the state of this particular bond. If $v$ is open, add it to the exploration; if it is closed, mark it as tested and never add it. Assign fresh independent random variables of the same parameter to every other bond, including bonds leading to vertices already tested. The choice of the next unassigned bond depends only on previously revealed information. Its assigned coin is therefore independent of that information, so the resulting bond configuration has exactly the product bond percolation law. Parallel bonds cause no difficulty; loops may be discarded.

Every reached site has an open parent bond leading from a previously reached site. Consequently the conditional site percolation cluster is contained in the bond percolation cluster. The connected graph is countable because it is a locally finite graph; taking increasing finite stages of the exploration proves the same containment for infinite clusters. Removing the conditioning on the root givesThus positive site percolation survival implies positive bond percolation survival at the same parameter, and infinite percolation susceptibility for site percolation implies infinite percolation susceptibility for bond percolation. Taking the infima in the definitions proves both comparisons:

For $r\ge 3$, join a clique on $r-2$ vertices to an independent set on $n-r+2$ vertices. The resulting complete $(r-1)$-partite graph hasIts largest clique has size $r-1$. Every missing edge joins two vertices of the independent set; adding it produces a $K_r$ with the original $(r-2)$-clique. It is therefore maximal $K_r$-free. For $n>r$ its part sizes are unbalanced, so its edge count is strictly less than that of $T_{r-1}(n)$.

For $r=3$ the construction is a star with $n-1$ edges. Every maximal triangle-free graph on $n>3$ vertices is connected: an edge between distinct components would create no triangle. A connected graph has at least $n-1$ edges, so the minimum is $n-1$, attained by the star. Literally the requested construction is impossible for $r=2$, since $T_1(n)$ has zero edges. The nontrivial assertion thus requires the customary $r\ge 3$ hypothesis.

Let $v_1\cdots v_k$ be a longest path. Every neighbour of either endpoint lies on it. Among the $k-1$ indices $i=1,\dots ,k-1$, mark those with $v_1{v}_{i+1}$ an edge and those with $v_i{v}_k$ an edge. Their total number isso some index has both marks. The edges at that index close the path into the cycle graphThe minimum-degree hypothesis also forces the graph to be a connected graph: each component would otherwise have at least $n/2+1$ vertices. If $k<n$, an edge from this cycle to an outside vertex would give a longer path by opening the cycle at its incident vertex. Hence $k=n$, proving the Dirac theorem and Hamiltonicity.

The bound is sharp. For $n=2m$, take two disjoint copies of $K_m$, with minimum degree $m-1$. For $n=2m+1$, take two copies of $K_{m+1}$ sharing exactly one vertex. The minimum degree is $m$, but the shared vertex is a cut vertex, which no Hamiltonian graph can have. Both constructions have

Each row of the oriented incidence matrix records one signed endpoint difference, henceReversing an edge changes the sign of its row, and permuting the edges permutes rows; neither operation changes the sum of absolute differences. Thus the graph total variation is independent of the chosen orientation and ordering. If $D\theta =0$, values agree at the endpoints of every edge. Any two graph vertices of a connected graph are joined by a graph path, so their values agree. Therefore $\ker D=\mathrm{span}\{1\}$.

Use simple random walk on the loopless undirected unweighted graph, with transition probability $1/\mathrm{deg}(x)$ to each graph neighbour, and take $a\ne z$. Set $T_z=\inf \{t\ge 0:X_t=z\}$. Returns to $a$ before visiting $z$ do not end the commute. After its first visit to $z$, stop at the subsequent first visit to $a$, so $\tau =T_z+T_a\circ {\vartheta }_{T_z}$. Count transitions with departure times $0\le t<\tau$, including the final step arriving at $a$. The expected hitting times are finite: in a finite connected graph there is a uniformly positive probability to reach any fixed target within a fixed number of steps, giving a geometric tail bound.

We prove the needed killed-walk occupation voltage identity from its visit balance equation. DefineFor $x\ne z$, counting arrivals before absorption givesHence, for the Graph Laplacian $Lv(x)=\mathrm{deg}(x)v(x)-{\sum }_{y\sim x}v(y)$, we have $Lv(x)=1_{\{x=a\}}$ away from $z$. Since the sum of all Laplacian coordinates is zero, $Lv(z)=-1$. Thus $v$ is the electrical voltage of a unit flow from $a$ to $z$, grounded at $z$, and $v(a)=R_{\mathrm{eff}}(a,z)$.

Construct $w(x)=g_{za}(x)/\mathrm{deg}(x)$ for the opposite killed leg. It satisfies $Lw={\delta }_z-{\delta }_a$ and $w(a)=0$. Therefore $L(v+w)=0$. The harmonic maximum principle on a finite graph makes $v+w$ constant: at a maximum its value equals the average over its graph neighbours, forcing all of them to share that value, and connectedness propagates it. At $a$ the constant equals $R_{\mathrm{eff}}(a,z)$.

For any oriented edge $(x,y)$, each visit to $x$ before the target hit produces a transition to $y$ with conditional probability $1/\mathrm{deg}(x)$. The expected traversals during the first killed leg are therefore $v(x)$, and those during the second leg are $w(x)$ by the Strong Markov property at $T_z$. This proves directed edge occupation in a random-walk commute:In particular, both directions of every unoriented edge have this same expected value, although their counts on an individual commute need not coincide. Summing over the $2|E|$ directed edges also recovers the commute time identity ${\mathbb{E}}_a\tau =2|E|R_{\mathrm{eff}}(a,z)$.

The distinct-terminal convention is necessary for the printed “returns” interpretation. If $a=z$ and $\tau$ is the first positive return, a graph with two graph vertices and one edge gives $S(a,y)=1$ while $R_{\mathrm{eff}}(a,a)=0$. Thus either take $a\ne z$, as intended, or explicitly define the degenerate commute as $\tau =0$. A general nonuniform random walk is not covered by the unweighted simple-walk formula.

Let $\mathcal{T}(G)$ be the finite set of spanning trees of the connected graph. A spanning tree has all the graph vertices, is connected, and contains no graph cycle. Connectedness ensures that at least one exists, for example by repeatedly deleting an edge of a graph cycle while retaining connectedness. The uniform spanning tree is the random edge set $T$ withEvery tree has $|V|-1$ edges, so uniformity is over complete trees, not over arbitrary subsets of that size. Such subsets can be disconnected or cyclic and do not belong to $\mathcal{T}(G)$. Nor are indicators of the tree edges generally independent. The matrix-tree theorem gives $|\mathcal{T}(G)|$ as any cofactor of the unweighted Graph Laplacian if a numerical normalizing constant is needed.

This definition uses the ordinary undirected unweighted graph convention. With positive conductances the analogous law for spanning trees weights $t$ proportionally to ${\prod }_{e\in t}{c}_e$ and is generally not uniform. When the graph has one graph vertex the empty edge set is the unique spanning tree, so its probability is one.

For finite $G$, choose a root $r$ and an ordering of the other graph vertices. Begin with a tree consisting only of $r$. At each stage take the first graph vertex not already in the tree, run an independent simple random walk from it until its first hit of the existing tree, erase the loops chronologically, and add the resulting graph path. In chronological loop erasure, revisiting a graph vertex already in the current list deletes all list entries after that graph vertex; otherwise append the new graph vertex. The retained final graph path is simple, with new internal graph vertices and exactly one endpoint in the old tree. Adding it therefore preserves connectedness and creates no graph cycle. Graph vertices already included are skipped. On a finite connected graph each walk hits the existing tree almost surely; after finitely many attachments all graph vertices are present.

The Wilson algorithm theorem states that this tree is a uniform spanning tree, independently of the chosen root and order. The required hypotheses here are an undirected connected finite graph and transition probabilities proportional to conductance; unit conductances give simple random walk and the uniform law. This is the sampling result being described, not a claim that arbitrary walk transition probabilities would produce a uniform tree.

For an infinite connected locally finite recurrent graph, choose a fixed finite root $r$ and enumerate all graph vertices. A connected locally finite graph is countable, since its finite-radius balls are finite. Run the same algorithm successively. Recurrence and irreducibility imply that a walk from any graph vertex hits $r$ almost surely, hence hits the current tree. Every attached graph path is therefore finite. The countable intersection of these probability-one events has probability one, and the increasing union is an acyclic connected subgraph containing all graph vertices: each graph vertex receives a finite route to $r$.

This gives the uniform spanning tree of a recurrent infinite graph. It is not a uniform counting measure on all infinite spanning trees. Equivalently, its joint probability distributions on finite collections of edges are the common limits of uniform spanning trees along any graph exhaustion, using the approximations defining the free uniform spanning forest and the wired uniform spanning forest. To check that equivalence, fix a finite edge set $F$ and run the chosen enumeration through a finite prefix containing all its endpoints. Once those graph vertices are in the tree, later attachments cannot add an edge with both endpoints already included, so membership of $F$ is settled permanently. Only finitely many random walks have been used; each has a finite trajectory almost surely. All their visited graph vertices and all their graph neighbours lie in a sufficiently large exhaustion set. Couple finite-volume and infinite-volume walks with the same steps there. Whether the exterior is deleted or wired to one boundary graph vertex, these initial trajectories and their loop erasures then agree. This proves convergence of the probabilities of cylinder sets determined by those edges.

The limiting law is unchanged by the root and ordering because the laws on finite graphs have this invariance. The same coupling shows that changing the exhaustion also leaves the law unchanged. ThusThe result is a single spanning tree because the graph is recurrent. On a transient graph, infinite-volume spanning-tree limits may instead be uniform spanning forests; recurrence cannot be omitted from this conclusion.

For a $\Delta$-regular undirected graph, its Graph Laplacian is $L=\Delta I-A$, and for any complex vector $v$,If $Av=\Delta v$, the left side is zero, hence $v_i=v_j$ on every edge. In a connected graph all coordinates coincide. Thus the eigenspace is exactly the constant vectors, of dimension one. Since the adjacency matrix of a graph is symmetric and diagonalizable,

For the Petersen graph, each vertex has three neighbours, adjacent vertices have no common neighbour, and distinct nonadjacent vertices have exactly one common neighbour. The entries of $A^2$ count length-two walks, so diagonal entries are three, adjacent entries zero, and other off-diagonal entries one. HenceThe connected graph has the simple eigenvalue three with eigenvector $1$. On its orthogonal complement $J=0$, so the remaining eigenvalues satisfy ${\lambda }^2+\lambda -2=0$ and are one or minus two. If their multiplicities are $r,s$, then $r+s=9$ and $\mathrm{tr}A=3+r-2s=0$. Thus

Use natural logarithms. A $K_t$ graph minor is represented by $t$ disjoint nonempty branch sets of a graph minor, each inducing a connected graph, with an edge between every pair. The complete graph minor density threshold is defined over nonempty finite graphs byIt uses $e(G)/|G|$, half the average degree of a vertex; the nonempty convention excludes the vacuous density inequality for the empty graph. The PDF has the intended definition with a universal implication over $G$; the TeX transcription corrupts that definition.

Probabilistic lower bound. Put $N=\lfloor t\sqrt{\log t}/4\rfloor$ and take the binomial random graph $G(N,1/2)$. Its edge count has mean $N(N-1)/4$. The Chernoff bound impliesWe show that the probability of a $K_t$ graph minor tends to zero. Ignore connectedness, which only enlarges the set of possible models, and assign each of the $N$ vertices one of $t$ branch labels or an unused label. There are at most $(t+1{)}^N$ assignments.

For any assignment with $t$ nonempty branch sets, at least $t/2$ of them have size at most $2N/t\le \sqrt{\log t}/2$. Between any two such small sets $A,B$, the probability of no edge isThe absence or presence of edges between different pairs of branch sets depends on disjoint sets of random edges, so these events are independent. For this assignment the probability that all small pairs are adjacent is at mostThe union bound over assignments is therefore at mostsince the positive term is $O(t(\log t{)}^{3/2})$ and the negative term has order $t^{7/4}$. There is consequently a graph with no $K_t$ graph minor and with $e(G)/N\ge N/8$. Any constant having the universal forcing property must exceed this ratio. Since $N/8\ge t\sqrt{\log t}/64$ for large $t$,

The auxiliary density lemma contains a typo. Both the original PDF and the TeX print $e(G)\le 11k|G|$ in the hint. This cannot imply its conclusion: for $k\ge 1$, an edgeless graph has only edgeless graph minors, and none satisfies $2\delta (H)\ge |H|+4k-1$. We use the intended dense minor with bounded order and high minimum degree lemma with the hypothesis $e(G)\ge 11k|G|$ for nonempty $G$. The upper-bound argument below depends on that corrected supplied lemma; the literal printed hint is false.

Probabilistic upper bound and the constant seven. Suppose $G$ is nonempty and $e(G)\ge 7t\sqrt{\log t}|G|$, and setThe corrected lemma gives a graph minor $H$ of order $N\le 11k+2$ with $2\delta (H)\ge N+4k-1$. Since $\delta (H)\le N-1$, we also have $N\ge 4k+1$. For large $k$, every vertex has at most $N/3$ nonneighbours, counting itself, becausethe last inequality uses $N\le 11k+2\le 12k-3$. Moreover any two distinct vertices have at leastcommon neighbours.

Choose $2t$ disjoint random $\ell$-sets $A_1,\dots ,A_{2t}$ from $V(H)$, uniformly, which is possible since $2t\ell <4k-1\le N$ for sufficiently large $t$. For a set $A$, let $U(A)$ be the set of vertices having no neighbour in $A$. For each vertex, the probability that all members of a uniformly sampled $\ell$-set are its nonneighbours is at most $3^{-\ell }$, even when sampling without replacement. By linearity of expected value,Call $A$ good if $|U(A)|\le N{3}^{-19\ell /20}$. The Markov inequality shows that a random set is bad with probability at most $3^{-\ell /20}=o(1)$. Thus the expected number of bad sets among our $2t$ samples is $o(t)$.

Conditional on a fixed good $A_i$, the marginal distribution of $A_j$ is uniform among $\ell$-sets outside $A_i$. For there to be no edge between them, all its members must lie in $U(A_i)$. HenceHere $(N/(N-\ell ){)}^{\ell }=\exp (O({\ell }^2/N))=1+o(1)$. Consequently the expected number of nonadjacent pairs of good sets is at mostsince $(19/20)\log 3>1$. Applying the Markov inequality to both counts shows that there exists a choice with fewer than $t$ bad sets and at most $t$ nonadjacent pairs of good sets. Select $t$ of the good sets; they still have at most $t$ missing adjacencies.

Make each selected set connected by fixing one of its vertices as a root and, for each of its other members, adding one unused common neighbour of that member and the root. This uses at most $t(\ell -1)$ additional vertices. Then for each pair with no original edge, choose one vertex in each set and add an unused common neighbour to one of the sets. The added vertex is joined to that set and to the other set, so it preserves connectedness and repairs their adjacency. At most $t$ such repairs are required.

Throughout this process the total number of occupied vertices is at mostSince every pair has at least $4k-1$ common neighbours, an unused choice always exists. The final sets are disjoint connected branch sets of a graph minor with every pair adjacent. They represent $K_t$ in $H$, and hence in $G$, by transitivity of graph minors. This random branch-set construction of a complete graph minor proves

The sharp linear threshold for a four-vertex complete minor. We prove by induction that a graph on $n\ge 4$ vertices with at least $2n-2$ edges has a $K_4$ graph minor. The case $n=4$ is $K_4$ itself. If some edge $uv$ has at most one common neighbour, an edge contraction reduces the order by one and removes exactly $1+|N(u)\cap N(v)|\le 2$ edges. The contracted graph has at least $2(n-1)-2$ edges, so induction applies.

Otherwise every edge is in at least two triangles in a graph. Choose a vertex $v$ incident with an edge. Each vertex $u\in N(v)$ has at least two neighbours of a vertex in $N(v)$, since these are the common neighbours of $u,v$. Thus $G[N(v)]$ has minimum degree of a graph at least two. A longest path in this finite graph has an endpoint adjacent to an earlier nonconsecutive path vertex, and hence contains a cycle in a graph. Together with $v$, this cycle in a graph gives a wheel graph as a subgraph. Divide its rim into three consecutive nonempty connected arcs; these arcs and $\{v\}$ form four pairwise adjacent branch sets of a graph minor. Therefore

With $2n-3$ edges the answer is no. Take the join of graphs of $K_2$ and an independent set of $n-2$ vertices. It has $1+2(n-2)=2n-3$ edges. Among four disjoint putative branch sets of a graph minor, at most two can contain the two vertices of $K_2$. Any connected branch set avoiding them is a singleton in the independent set. At least two such singleton sets would therefore have no edge between them. This excludes a $K_4$ graph minor for every $n\ge 4$ and proves sharpness.

Let $A$ be the adjacency matrix of a graph. Since $G$ is a regular graph of degree $d$, every row of $A$ sums to $d$. Therefore, for $\mathbf{e}=(1,\dots ,1{)}^T$,so $d$ is a graph eigenvalue.

If $Av=dv$, then the quadratic form of the Graph Laplacian $L=dI-A$ givesEvery summand is nonnegative, so $v_i=v_j$ along every edge. The graph is connected, hence all coordinates of $v$ are equal and $v$ is a scalar multiple of $\mathbf{e}$. Thus the $d$-eigenspace is one-dimensional. Since $A$ is a symmetric matrix, the spectral theorem for real symmetric matrices makes it diagonalizable, so the algebraic multiplicity is also one. This proves the constant eigenvector of a regular graph result.

The Coxeter graph has vertex set $I$. Distinct vertices $i,j$ are joined precisely when $m_{ij}\ge 3$, and the edge is labelled $m_{ij}$ when $m_{ij}>3$; the customary unlabelled edge therefore means $m_{ij}=3$. The Coxeter system is an Irreducible Coxeter system precisely when this graph is connected.

A finite simple graph that is a connected graph and a planar graph, with $n\ge 3$ vertices and $m$ edges, satisfies $m\le 3n-6$. Choose a plane graph: each facial boundary walk has length at least three, so $3f\le 2m$. Substitute $f=2-n+m$ from the Euler formula for a connected planar graph.^[ref]

The complete graph $K_4$ attains equality: $6=3\cdot 4-6$. The bound also holds for a disconnected finite simple graph that is a planar graph with $n\ge 3$: place its connected components of a graph in disjoint regions and join them with new bridges in a graph, then apply the connected bound.

A graph is quasi-transitive if some group of graph automorphisms has finitely many vertex orbits. This is also called a graph of finite type. A connected graph that is infinite and a locally finite graph then has uniformly bounded degrees. Every orbit is infinite: a finite invariant orbit would have finite neighborhoods, and finitely many orbit types would put the entire graph in one such neighborhood. This permits finite sets to be moved arbitrarily far apart by graph automorphisms.

A finite nonempty connected graph whose edges are undirected admits a strong orientation if and only if it has no bridge in a graph. This statement holds for finite multigraphs as well as finite simple graphs; self-loops do not help connect distinct vertices. The single-vertex case is allowed, with length-zero directed paths.^[ref]

Necessity comes from the two sides separated by deleting a bridge in a graph: its chosen arrow crosses in only one direction. For sufficiency, the cited constructive proof uses depth-first search to find a cycle in a graph, orients it cyclically, contracts it, and repeats. Undoing the contractions replaces junctions by directed cycles, retaining two-way reachability. Contraction can produce multiple edges, so these must remain distinct. This is a construction outline; the input graph is finite and bridgeless, and remaining edges within contracted pieces can receive arbitrary directions.^[ref]

A spanning tree of a connected graph $G$ is a subgraph that is a tree and contains every vertex of $G$. Its edges must come from $G$. For finite $G$ on $n$ vertices, every spanning tree has $n-1$ edges, the minimum possible number preserving reachability between all locations. This makes a spanning tree a compact certificate that the original graph is connected. A minimum spanning tree additionally minimizes a specified total edge weight; merely selecting a spanning tree supplies no such optimization. ^[ref]

Let $G$ be our seven-vertex tree with edge $57$ added. It has seven edges and a graph cycle. Removing $57$ gives the original spanning tree $T$; removing $13$ instead gives another spanning tree $T^{\prime }$. Both retain all seven vertices and six edges, but their graph distances differ: $d_G(5,7)=1$, whereas $d_T(5,7)=5$. In $T^{\prime }$, $d_{T^{\prime }}(5,7)=1$ but $d_{T^{\prime }}(1,3)=5$, along $1,2,5,7,6,3$, compared with $d_G(1,3)=1$. Preserving reachability permits longer journeys; breadth-first search can preserve graph distances from one chosen starting vertex.

To construct a spanning tree, repeatedly delete any edge on a graph cycle. Its endpoints remain joined through the rest of that graph cycle, so the deletion preserves connectedness. Finiteness ensures that this stops at a connected acyclic graph. Conversely, adding an omitted edge to a spanning tree creates its unique fundamental cycle; deleting any other edge on that graph cycle produces another spanning tree. The displayed exchange replaces $13$ by $57$. ^[ref]

Associate a transposition to each edge of a finite connected graph. For a simple path $v_0,\dots ,v_{\ell }$, let $t_j=(v_{j-1}{v}_j)$. Then $t_1\cdots t_{\ell -1}{t}_{\ell }{t}_{\ell -1}\cdots t_1=(v_0{v}_{\ell })$, using rightmost-first composition. Thus edge transpositions generate every transposition, and hence the full symmetric group.

A tree is a nonempty connected graph containing no graph cycle. Think of seven locations joined so that everyone can reach everyone else, but no connection supplies an alternative route. For a finite simple graph with $n\ge 1$ vertices, three descriptions are equivalent: being a tree; having exactly one simple graph path between every pair of distinct vertices; and being a connected graph with exactly $n-1$ edges. Here a simple graph path repeats no vertex. Connectedness in the last description is essential. ^[ref]

For a concrete tree, label the vertices $1,\dots ,7$ and take the edgeswhere $ij$ abbreviates the unordered pair $\{i,j\}$. There are seven vertices and six edges. The unique graph path from $4$ to $7$ is $4,2,1,3,6,7$, using five edges. The degree sequence in label order is $(2,3,2,1,1,2,1)$: its sum is $12=2\cdot 6$, as the degree-sum formula predicts. Each of $4,5,7$ is a leaf of a graph, having vertex degree one.

Deleting edge $13$ leaves precisely two connected components of a graph, on $\{1,2,4,5\}$ and $\{3,6,7\}$. Every edge of a tree is a bridge in a graph. Instead add edge $57$ to the original tree: it closes the unique existing graph path $5,2,1,3,6,7$ into exactly one graph cycle. Thus a tree provides economical connections with no redundancy: a single failed link separates locations. Every finite tree with at least two vertices has at least two instances of a leaf of a graph; the endpoints of a longest graph path supply them. ^[ref]

The number $n-1$ alone proves nothing about connectedness: a cycle graph on $1,\dots ,6$ together with an isolated vertex $7$ has seven vertices and six edges, yet is disconnected. Briefly, two different simple graph paths between the same vertices would contain a graph cycle. Repeatedly removing a leaf of a graph and its incident edge proves the tree count $n-1$ by mathematical induction, starting with one vertex and no edges.