Growing-clique overlap bound ($r\le n^{1/3},{\mu }_r\to \infty ,{\mu }_{r+1}\to 0⟹\mathrm{Var}{X}_r=o({\mu }_r^2)$)

Write ${\mu }_r=\left(\genfrac{}{}{0ex}{}{n}{r}\right)p^{\left(\genfrac{}{}{0ex}{}{r}{2}\right)}$ and $T_s=\left(\genfrac{}{}{0ex}{}{r}{s}\right)\left(\genfrac{}{}{0ex}{}{n-r}{r-s}\right)p^{-\left(\genfrac{}{}{0ex}{}{s}{2}\right)}/\left(\genfrac{}{}{0ex}{}{n}{r}\right)$. The first mean condition implies $p^{-1}\le (en/r{)}^{2/(r-1)}$ eventually. For $2\le s\le r/2$, this yields $T_s\le (C/s{)}^s$, and each fixed-$s$ term tends to zero when $n\ge r^3$. The second mean condition implies $p\le ((r+1)/n{)}^{2/r}$ eventually. Writing $s=r-j$ gives $T_{r-j}={\mu }_r^{-1}\left(\genfrac{}{}{0ex}{}{r}{j}\right)\left(\genfrac{}{}{0ex}{}{n-r}{j}\right)p^{j(2r-j-1)/2}\le {\mu }_r^{-1}(C/j{)}^j$ for $1\le j\le r/2$, and $T_r={\mu }_r^{-1}$. Both bounding series are summable. The overlap formula for the variance of a clique count therefore gives relative variance tending to zero; the second moment method proves an $r$-clique exists with high probability.