Counting cliques and clique covers in random graphs
  We study the problem of counting the number of {\em isomorphic} copies of a given {\em template} graph, say $H$, in the input {\em base} graph, say $G$. In general, it is believed that polynomial time algorithms that solve this problem exactly are unlikely to exist. So, a lot of work has gone into designing efficient {\em approximation schemes}, especially, when $H$ is a perfect matching.   In this work, we present efficient approximation schemes to count $k$-Cliques, $k$-Independent sets and $k$-Clique covers in random graphs. We present {\em fully polynomial time randomized approximation schemes} (fpras) to count $k$-Cliques and $k$-Independent sets in a random graph on $n$ vertices when $k$ is at most $(1+o(1))\log n$, and $k$-Clique covers when $k$ is a constant. [Grimmett and McDiarmid, 1975] present a simple greedy algorithm that {\em detects} a clique (independent set) of size $(1+o(1))\log_2 n$ in $G\in \mathcal{G}(n,\frac{1}{2})$ with high probability. No algorithm is known to detect a clique or an independent set of larger size with non-vanishing probability. Furthermore, [Coja-Oghlan and Efthymiou, 2011] present some evidence that one cannot hope to easily improve a similar, almost 40 years old bound for sparse random graphs. Therefore, our results are unlikely to be easily improved.   We use a novel approach to obtain a recurrence corresponding to the variance of each estimator. Then we upper bound the variance using the corresponding recurrence. This leads us to obtain a polynomial upper bound on the critical ratio. As an aside, we also obtain an alternate derivation of the closed form expression for the $k$-th moment of a binomial random variable using our techniques. The previous derivation [Knoblauch (2008)] was based on the moment generating function of a binomial random variable. 
Kashyap Dixit and Martin F\"urer
cs.DS