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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.

If $X$ counts triangles in $G(n,p)$, thenandThus $np\to \infty$ implies $\mathrm{var}X/(\mathbb{E}X{)}^2\to 0$, and the second moment method gives $\mathbb{P}(X>0)\to 1$.

If $np\to 0$, the triangle count in a binomial random graph has expectation at most $(np{)}^3/6\to 0$, so the Markov inequality shows there are no triangles in a graph with probability tending to one. If $np\to \infty$, the triangle variance in a binomial random graph divided by the squared mean is $O((np{)}^{-3})+O((n^{2}p{)}^{-1})\to 0$. The second moment method then gives at least one triangle in a graph with probability tending to one. At the intermediate scale $np\to c\in (0,\infty )$, a nontrivial Poisson limit for triangles in a binomial random graph describes the count.