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It suffices to prove the assertion after replacing $\omega$ by $\min \{\omega ,\sqrt{\log n}\}$: that replacement gives a tighter interval. Thus assume $\omega \to \infty$ and $\omega =o(\log n)$, and putAt $p_{-}$, let $J$ count isolated vertices in $L$. The same indicator random variable calculation as for all isolated vertices givesBy the Chebyshev inequality, $J>0$ with high probability. Since $\ell \ge 2$ eventually, the marked vertices are then not all connected.

At $p_{+}$, the logarithmic-regime giant component result applies with $k=2$ and any fixed ${\gamma }_0\in (1/3,1/2)$. Its unique giant component misses $(1+o(1))n^{1/2}{e}^{-\omega /4}$ vertices. On the event that it misses at most twice that number, exchangeability makes the missed vertex set uniform conditional on its size. A union bound therefore givesThus all of $L$ is connected at $p_{+}$ with high probability.

For the uniform random graph process, assign uniform labels to the edges of the complete graph as independent random variables and reveal them in label order. The binomial random graph $G(n,p)$ is the prefix containing the $M(p)$ labels at most $p$, where $M(p)$ has a binomial distribution with parameters $N=\left(\genfrac{}{}{0ex}{}{n}{2}\right)$ and $p$. Its variance at $p_{\pm }$ is $O(n\log n)$, whereas the gap between $\mathbb{E}M(p_{\pm })$ and the relevant endpoint $m_{\pm }$ is $(1+o(1))\omega n/8$. The Chebyshev inequality gives $M(p_{-})\ge m_{-}$ and $M(p_{+})\le m_{+}$ with high probability, with harmless integer rounding. The monotone graph property that all marked vertices are connected now impliesThe marked-set connectivity threshold is lower than the threshold for connecting every vertex, because only about $\sqrt{n}$ specified vertices need to avoid the small graph components.

Put $d=np=\gamma \log n$ and $\mu =n^{1-\gamma }$. The hypothesis implies $\mu \ge e^{\omega (n)}\to \infty$. For the number $I$ of isolated vertices, the isolated vertices in the Erdős-Rényi model formulas giveThus the Chebyshev inequality gives $I=(1+o(1))\mu$ with high probability, uniformly over the specified $\gamma$ range.

The Cayley formula supplies a spanning tree on any connected vertex set. Consequently the expected value $C_j$ of the number of graph components of order $j\ge 2$ is bounded byChoose a fixed $\eta \in (0,1/2)$ with ${\gamma }_0(1-\eta )(k+1)>1$. For $k+1\le j\le \eta n$, with $j\ge 2$, the binomial coefficient bound $\left(\genfrac{}{}{0ex}{}{n}{j}\right)\le (en/j{)}^j$ givesHere $ed{e}^{-d(1-\eta )}\le e\log n{n}^{-{\gamma }_0(1-\eta )}\to 0$. The geometric series and a union bound therefore exclude this entire size range, since $n[e\log n{n}^{-{\gamma }_0(1-\eta )}{]}^{k+1}=o(1)$. For $\eta n\le j\le n/2$, an empty graph cut would be necessary. Its total probability is at mostThere are consequently no graph components of orders between $k+1$ and $n/2$, with high probability.

For each fixed $2\le j\le k$, a graph component containing a graph cycle has a spanning tree and at least one extra edge. Counting that extra edge gives an expected value $O_k(d^j{e}^{-dj})=o(1)$; hence every small graph component is a tree component. The total number $R_2$ of vertices in tree components of orders $2,\dots ,k$ satisfiesThe Markov inequality gives $R_2=o(\mu )$ with high probability. When $k=1$, this sum is empty and $R_2=0$. Since $\mu =o(n)$, the remaining vertices must lie in one graph component larger than $n/2$. It is unique, and its order is $n-(1+o(1))n^{1-\gamma }$. Every other graph component is a tree with at most $k$ vertices. This is the logarithmic-regime giant component mechanism: almost all missing vertices are isolated.

Assume $0<\lambda =1-\epsilon <1$, as required for this nontrivial subcritical binomial random graph. Put $\delta =\lambda -1-\log \lambda >0$. A breadth-first exploration of a binomial random graph is dominated by a Galton-Watson process whose offspring counts are independent random variables with law $\mathrm{Bin}(n,\lambda /n)$. If its total progeny $T$ is at least $j$, the first $j-1$ offspring counts sum to at least $j-1$. Their moment-generating function is bounded by that of a Poisson distribution of mean $\lambda (j-1)$. The exponential Markov inequality, optimized at $t=-\log \lambda$, yieldsTaking $j=\lceil (1+\eta )\log n/\delta \rceil$ for any fixed $\eta >0$, a union bound over all starting vertices gives $\mathbb{P}(L_1\ge j)=o(1)$.

For the matching lower bound, let $X_j$ count tree components of order $j$. The tree-component expectation in the Erdős-Rényi model is exactlyFor $j=O(\log n)$, the Stirling formula givesChoose $j=\lfloor (1-\eta )\log n/\delta \rfloor$, with $0<\eta <1$. Then $\mathbb{E}{X}_j\to \infty$. Distinct overlapping vertex sets cannot both be graph components. For disjoint sets, the joint probability differs from the product only because the $j^2$ between-set edges were counted twice as absent. ThusIt follows that $\mathrm{var}{X}_j/(\mathbb{E}{X}_j{)}^2=o(1)$. The second moment method gives $X_j>0$ with high probability. Combining both bounds, and then taking arbitrarily small fixed $\eta$, proves the sharp subcritical largest-component scaleFinally the Taylor expansion of $-\log (1-\epsilon )$ gives $\delta ={\epsilon }^2/2+{\epsilon }^3/3+\cdots$. The endpoint $\lambda =0$ has no edges and is excluded from this logarithmic asymptotic.

Write $\mu =1+\epsilon$, $p=\mu /n$, $q=1-p$, and let $T$ be the total progeny of a binomial branching process. The preceding binomial branching survival correction gives $\rho =(2+o(1))\epsilon$. For a branching process conditioned on extinction, the offspring probability generating function is $f((1-\rho )s)/(1-\rho )$, with meanfor sufficiently large $n$. Summing expected generation sizes therefore gives $\mathbb{E}[T;T<\infty ]=(1-\rho )/(1-f^{\prime }(1-\rho ))=O(1/\epsilon )$.

Put $K=\lceil {\epsilon }^{-3}\rceil$ and let $N_{\ge K}$ count vertices in graph components of order at least $K$. A breadth-first exploration of a binomial random graph is dominated by $T$. The Markov inequality applied to finite total progeny givesAlways $L_1\le K+N_{\ge K}$. Since $n{\epsilon }^4\ge n^{1/3}\to \infty$, we have $K=o(\epsilon n)$ and $n/(\epsilon K)=O(n{\epsilon }^2)=o(\epsilon n)$. Hence $\mathbb{E}{L}_1\le (2+o(1))\epsilon n$. This bound controls the expected value directly, including rare large graph components.

For the lower bound, use the breadth-first exploration of a binomial random graph with $U_t$ unseen vertices, $A_t$ active vertices and $t$ explored vertices, starting from $U_0=n$, $A_0=0$. Let $b_t=1_{\{A_{t-1}=0\}}$ indicate a new root. Conditional on the past, the newly discovered count $Z_t$ has a binomial distribution $\mathrm{Bin}(U_{t-1}-b_t,p)$. Put $D_t=Z_t-p(U_{t-1}-b_t)$. The unseen-count recursion givesHere $M_t$ is a martingale. Up to $T_0=\lceil 3\epsilon n\rceil$, martingale-difference orthogonality gives $\mathbb{E}{M}_{T_0}^2\le C\epsilon n$, since each conditional variance is at most $np<2$ and $q^{-2T_0}$ is bounded.

Take $h=\sqrt{\epsilon }+(n{\epsilon }^3{)}^{-1/8}$. Then $h\to 0$, $\epsilon =o(h)$ and $h^{2}n{\epsilon }^3\to \infty$. The Doob L2 maximal inequality impliesThe Taylor expansion, uniformly for $t\le T_0$, gives $F(t)=\epsilon t-t^2/(2n)+O({\epsilon }^{3}n+\epsilon )$. Thus $F(t)\ge h{\epsilon }^{2}n/3$ throughout the integer interval $[\lceil h\epsilon n\rceil ,\lfloor (2-h)\epsilon n\rfloor ]$ for large $n$. The new-root sum is nonnegative, so on the preceding event $A_t>0$ throughout this interval. No graph component finishes there: all these explored vertices belong to one graph component, of order at least $(2-2h)\epsilon n-O(1)$, with high probability. Its expected value is therefore at least $(2-o(1))\epsilon n$. Combining both inequalities provesThe barely-supercritical largest-component expectation uses both a positive exploration window and a finite-progeny bound; a bare convergence-in-probability assertion would not by itself justify this expectation.

The persistence of unsampled graph components prevents the mass from each size band from disappearing entirely before $t_c$. More precisely, a band $[k,Dk)$ containing at least $\delta n/4$ vertices retains at least $\beta n$ vertices in that same band after at most $n/k$ steps, with high probability, for a fixed $\beta >0$ depending only on $\delta ,D$. To see why, each initial graph component in that band has a fixed positive probability that none of the offered edge endpoints touches it during the interval. Such a graph component remains unchanged regardless of the selection rule. The McDiarmid inequality concentrates the total mass of these untouched graph components. The reusable lemma supplies the full uniform-in-starting-time argument, including the version that samples absent edges.

Choose finitely many disjoint bands $[D^j,D^{j+1})$, $j=0,\dots ,J$, with $(J+1)\beta >1$. Part (i) provides at least $\delta n/4$ vertices in band $j$ at its time $t_{D^j}$. Each interval from that time to $t_c$ has length at most $n/D^j$, so persistence leaves at least $\beta n$ vertices in every one of these disjoint bands at the common time $t_c$. A union bound over this fixed finite number of bands makes all the conclusions simultaneous. Their total exceeds $n$, a contradiction. No fixed two-choice Achlioptas rule satisfies the explosive percolation hypothesis.

This continuity of fixed-choice percolation is the fixed-choice obstruction proved by Riordan and Warnke; their original research paper also treats a broader class of rules. The argument here does not apply when the number of offered choices grows with $n$.

We need independent sets in every sufficiently large remaining induced subgraph, rather than just one in the initial random graph. The useful concentration variable is the maximum edge-disjoint clique packing in the complement graph; changing one edge changes that variable by at most one. This avoids the much larger sensitivity of the ordinary clique count in a binomial random graph.

First work in a binomial random graph $H\sim G(m,1/2)$, take $r=r_0(m)$, and let $X$ count its $r$-cliques. Put $\mu =\mathbb{E}X\ge m^{9/5}$ and let $Z$ be its maximum edge-disjoint clique packing size. Form the clique conflict graph whose vertices are these cliques, adjacent when they share an edge. If $Q$ counts unordered conflicting pairs, the Caro-Wei bound gives, for each realization,To verify the bound, randomly order the vertices of a finite graph and retain each vertex preceding all its graph neighbours. These retained vertices are an independent set, with expected size ${\sum }_v(1+\mathrm{deg}v{)}^{-1}\ge X^2/(X+2Q)$ by the Cauchy-Schwarz inequality. Interpret the ratio as zero when $X=0$. A second application of the Cauchy-Schwarz inequality, now to expectations, givesCounting ordered pairs of $r$-sets with $j$ common vertices, including identical sets when $j=r$, yieldsThe shared edges account for the factor $2^{\left(\genfrac{}{}{0ex}{}{j}{2}\right)}$. We claimHere are the estimates, including the large overlaps. For $2\le j\le \lfloor r/2\rfloor$, the probability that a random $r$-set contains any specified $j$ elements is at most $(r/(m-r){)}^j$. Summing over their choices inside the first $r$-set givesThe logarithm of $T_j$ is a convex function, a quadratic in $j$, so the maximum on this interval is at an endpoint. We have $T_2=O(r^4/m^2)$. As $r=(2+o(1)){\log }_{2}m$, the other endpoint satisfiesand is smaller than $T_2$ for sufficiently large $m$. Summing at most $r$ terms gives $O(r^5/m^2)$.

For the other half of the overlaps, write $j=r-s$, where $0\le s\le \lceil r/2\rceil$. The exact identity and an upper bound areThe quantity in parentheses is $m^{-1/2+o(1)}$, which tends to zero. The geometric series is thus at most $2/\mu$ for sufficiently large $m$. This proves the claim. In fact $r^5/m^2=o(m^{-9/5})$ and $1/\mu \le m^{-9/5}$, so eventuallyNow expose the $N=\left(\genfrac{}{}{0ex}{}{m}{2}\right)$ independent edge indicators one at a time. The edge-exposure martingale $M_i=\mathbb{E}[Z\mid \text{first }i\text{ indicators}]$ starts at $\mathbb{E}Z$ and ends at $Z$. Deleting one edge destroys at most one member of any edge-disjoint clique packing. Hence changing one indicator changes $Z$ by at most one; coupling the remaining indicators shows $|M_i-M_{i-1}|\le 1$.

Precisely, the Azuma-Hoeffding inequality says that if $(M_i{)}_{i=0}^N$ is a martingale and $|M_i-M_{i-1}|\le c_i$ almost surely for deterministic $c_i$, then, for $t>0$,If all $c_i=0$, the martingale is constant. Applying the lower-tail inequality with $c_i=1$ and $t=\mathbb{E}Z$ givesfor sufficiently large $m$. This is the only martingale concentration inequality needed.

Return to the original binomial random graph $G\sim G(n,1/2)$ and set $m=\lceil n/({\log }_{2}n{)}^2\rceil$. For each fixed $m$-element vertex set $U$, the complement graph of $G[U]$ has distribution $G(m,1/2)$. The union bound therefore givessince $m^{8/5}/n\to \infty$. Thus with high probability every vertex set of size at least $m$ contains an independent set of sizeApply greedy colouring by removing independent sets: remove such an independent set, give it a fresh colour, and repeat while at least $m$ vertices remain. Give each of the fewer than $m$ final vertices its own colour. This uses at mostcolours, since $m=o(n/{\log }_{2}n)$. Together with the lower bound, this gives the chromatic number of the half-density binomial random graph:

Throughout this question the binomial random graph has $p=1/2$, as fixed in the introduction. Let $L={\log }_{2}n$ and $s=\lceil 2L\rceil$. Count the $s$-vertex independent sets by a random variable $I_s$. Linearity of expectation givesFor the second inequality, $s\ge 2L$ implies $sL-s(s-1)/2\le s/2\le L+1/2$. For the limit, $s!\ge (s/2{)}^{\lfloor s/2\rfloor }$ grows faster than $n$. By the first moment method, with high probability there is no such independent set; any larger independent set would contain one. Thus the independence number satisfies $\alpha (G)\le s-1<2L$ with high probability.

Every class of a proper graph coloring is an independent set, giving the claimed chromatic lower bound:

Let $X_s$ count the copies of the complete graph $K_s$ in the binomial random graph $G_{n,p}$. By linearity of the expected value and independence of distinct edges,Throughout, $n$ is sufficiently large that $k=\lfloor \log \log n\rfloor \ge 2$. We have $k\to \infty$, $k=O(\log \log n)$ and $k\log k=o(\log n)$.

First, since $p=n^{-2/k}$,The first moment method proves $\mathbb{P}(X_{k+1}>0)=o(1)$.

For the existence of a $k$-clique, put $X=X_k$ and $\mu =\mathbb{E}X$. Since $p^{\left(\genfrac{}{}{0ex}{}{k}{2}\right)}=n^{-(k-1)}$,This alone is insufficient: we must control the variance via the overlap formula for the variance of a clique count. Let $I_S$ indicate that a given $k$-set $S$ spans a clique. If $|S\cap T|=j$, the two copies share $\left(\genfrac{}{}{0ex}{}{j}{2}\right)$ edges, soFor $j=0,1$ their edge sets are disjoint and the covariance vanishes, even when they share one vertex. Counting ordered pairs of $k$-sets gives the exact formulaThis includes the diagonal case $j=k$.

To bound the overlap factor, regard it as the probability that a uniformly chosen $k$-set $T$ intersects a fixed $S$ in exactly $j$ vertices. A union bound over the possible specified $j$-subsets of $S$ yieldsHere $(a{)}_j=a(a-1)\cdots (a-j+1)$ is a falling factorial, and the last bound holds for $n\ge 2k$. ThereforeFor $2\le j\le k$, the concave quadratic $j(k-j+1)$ is minimized at an endpoint. Its endpoint values are $2(k-1)$ and $k$, both at least $k$. Thus every power of $n$ in the last sum is at most $n^{-1}$, andbecause the logarithm of its numerator is $O(k\log k)=o(\log n)$. The second moment method, or Chebyshev's inequality directly, gives $\mathbb{P}(X=0)=o(1)$. Finally, every larger clique contains a $(k+1)$-clique, so the absence of $K_{k+1}$ excludes all larger sizes. The clique number is exactly $k$ with high probability:

Put $p=C{n}^{-3/4}$. For any fixed $U\subseteq [n]$ with $|U|\ge 3n/5$, let $X_U$ count copies of the cycle graph $C_4$ in $G[U]$. ThenPairs of distinct $4$-cycles are dependent only when they share an edge. Classifying them by their common paths givesThe first Janson inequality therefore givesfor an absolute $c>0$ and all sufficiently large $n$. Choose $C$ so that $c{C}^4>\log 2+1$. A union bound over the at most $2^n$ choices of $U$ shows that, with high probability, every set of at least $3n/5$ vertices contains a $C_4$.

Now greedily choose vertex-disjoint $4$-cycles until none remains. If fewer than $n/10$ were chosen, they would cover fewer than $2n/5$ vertices, leaving more than $3n/5$ vertices and hence another $C_4$. This contradiction proves that the greedy packing contains at least $n/10$ vertex-disjoint copies.

Use sprinkling of a binomial random graph to write $G(n,p)=G_1\cup G_2$, where the rounds are independent,and $C$ is chosen large enough that $1-p=(1-p_1)(1-p_2)$. By the given theorem, $G_1$ has a Hamilton cycle $v_1{v}_2\cdots v_n{v}_1$ with high probability.

Condition on such a cycle. For each $3\le \ell \le n-1$, every chord $v_i{v}_{i+\ell -1}$ closes one of the two paths around the Hamilton cycle into a cycle of length $\ell$. There are at least $n/2$ distinct candidate chords, so the probability that $G_2$ supplies none is at mostA union bound over the fewer than $n$ lengths shows that all these cycles occur simultaneously with high probability when $c>4$. The Hamilton cycle itself supplies length $n$, so $G$ is pancyclic.

Fix the requested $\epsilon >0$ and choose much smaller constants $\eta ,\delta ,\rho >0$ in that order. Apply the Szemerédi regularity lemma with regularity parameter $\rho$ and a sufficiently large lower bound $m_0$ to a largest triangle-free subgraph $F\subseteq G$. Letbe the resulting equitable partition, and form the reduced graph of a regularity partition $R$ on $[m]$ by joining $i$ and $j$ when $(V_i,V_j)$ is $\rho$-uniform in $F$ and has $F$-density at least $\delta$.

Choose $\rho <\delta /2$. If $R$ contained a triangle, the triangle embedding lemma for regular pairs would give a triangle in $F$. Thus $R$ is triangle-free, and Turan theorem gives

Part (c), with error $\eta$, holds simultaneously for every pair of sets of size at least $n/\log n$. Since $m$ is bounded independently of $n$, all regularity classes and their halves satisfy this size condition for large $n$. It follows that every cross-pair contains at most $(p+\eta )|V_i||V_j|$ edges of $G$. It also givestake a bipartition of $V_i$ carrying at least half its internal edges and apply the cross-pair estimate.

Now count the edges of $F$. Pairs represented in $R$ contribute at mostThe exceptional set, the at most $\rho m^2$ irregular pairs, pairs of $F$-density below $\delta$, and edges inside classes together contribute at mostChoose $\eta$, then $\delta ,ho$, and finally $m_0^{-1}$ small enough in terms of $p\epsilon$. The total is at mostwith high probability, as required.

A connected graph on $s$ vertices containing at least two cycles has a spanning tree together with at least two further edges. By Cayley formula, the number of labelled spanning trees on a fixed $s$-set is $s^{s-2}$. Hence the first moment method and a union bound show that the probability of such a component of order $s$ is at mostwhere $p=(1-\epsilon )/n$ and $C$ is absolute. Requiring that the chosen set be a component would only add absent-edge conditions, so omitting them is a valid upper bound.

ThereforeThus every component in the stated range is either a tree or a unicyclic component with high probability.

The sharp Hamilton cycle threshold for the Erdős-Rényi model says thatonly above the window $np=\log n+\log \log n+o(1)$. The hypothesis therefore places $p(n)$ above that window. The Hamiltonicity-to-pancyclicity sprinkling principle then says that three independent $G(n,p(n))$ rounds contain every cycle graph $C_{\ell }$, $3\le \ell \le n$, with high probability: one round supplies a Hamilton cycle, while the other two supply the chords and short-cycle edges used to obtain all intermediate lengths.

The union of the three rounds has individual edge probabilityBy the standard monotone coupling, it is a subgraph of $G(n,3p(n))$. Since being pancyclic is an increasing graph property, the required probability tends to one. If $3p(n)>1$, interpret the latter parameter as $\min \{3p(n),1\}$, in which case the conclusion is immediate.

Fix positive $\alpha$, $D\ge 2$ and $B>0$. Suppose a size band $[k,Dk)$ of an Achlioptas process contains at least $\alpha n$ vertices at time $m$. For every fixed $k$, at least $\beta n$ of those vertices remain in unchanged graph components throughout the next $T=\lceil Bn/k\rceil$ steps, with high probability, where one may take $\beta =\alpha e^{-16BD}/4$. This is a deliberately nonoptimal positive constant.

For independent uniform candidate edges, an initial graph component with $w<Dk$ vertices is untouched if none of the $2T$ candidate edges meets it. A uniform edge hits it with probability at most $3w/n$. For sufficiently large $n$, $(1-3w/n{)}^{2T}\ge e^{-16BD}$, so the expected value of the untouched vertex mass $Y$ is at least $\alpha e^{-16BD}n$. Replacing one row of two candidate edges can change $Y$ by at most $8Dk$, because only the initial graph components hit by an old or new endpoint can change status. The McDiarmid inequality therefore makes $Y\ge \alpha e^{-16BD}n/2$ except on an event of probability $e^{-cn/k}$, for a positive constant $c$ depending only on $\alpha ,B,D$. An untouched graph component stays unchanged whichever offered edge is selected.

For candidates restricted to absent edges, generate each by an independent uniform proposal stream and reject already present edges. During any interval of $O(n)$ steps with $O(n)$ present edges, each proposal has rejection probability $O(1/n)$ conditional on the past. The number of rejections is at most $O(\log n)$ except on an event of probability smaller than any inverse power of $n$: if there were $r$ rejections among $2T+r$ proposals, a union bound over their positions bounds this by $\left(\genfrac{}{}{0ex}{}{2T+r}{r}\right)(C/n{)}^r$. The first $2T$ proposals are independent and give the previous untouched-mass estimate. Extra proposals can touch at most $2r$ additional initial graph components, losing only $O(Dk\log n)=o(n)$ vertices for fixed $k$. This leaves the stated $\beta n$ bound. A union bound makes these estimates simultaneous over starting times $m\le 3n$. They hold through the entire interval, so deterministic or random stopping times within it are allowed.

For fixed $0<\lambda <1$ in $G(n,\lambda /n)$, the largest component of a graph has the displayed order with high probability. A dominating Galton-Watson process gives the tail bound $\mathbb{P}(T\ge j)\le e^{-(\lambda -1-\log \lambda )(j-1)}$ by the exponential Markov inequality. For the lower bound, the tree-component expectation in the Erdős-Rényi model diverges at $j=\lfloor (1-\eta )\log n/(\lambda -1-\log \lambda )\rfloor$. The disjoint-set covariance factor and the exclusion of overlaps give relative variance tending to zero, so the second moment method supplies a tree component of that order.