  Graphlets are small connected induced subgraphs of a larger graph $G$.
Graphlets are now commonly used to quantify local and global topology of
networks in the field. Methods exist to exhaustively enumerate all graphlets
(and their orbits) in large networks as efficiently as possible using orbit
counting equations. However, the number of graphlets in $G$ is exponential in
both the number of nodes and edges in $G$. Enumerating them all is already
unacceptably expensive on existing large networks, and the problem will only
get worse as networks continue to grow in size and density. Here we introduce
an efficient method designed to aid statistical sampling of graphlets up to
size $k=8$ from a large network. We define graphettes as the generalization of
graphlets allowing for disconnected graphlets. Given a particular (undirected)
graphette $g$, we introduce the idea of the canonical graphette $\mathcal K(g)$
as a representative member of the isomorphism group $Iso(g)$ of $g$. We compute
the mapping $\mathcal K$, in the form of a lookup table, from all
$2^{k(k-1)/2}$ undirected graphettes $g$ of size $k\le 8$ to their canonical
representatives $\mathcal K(g)$, as well as the permutation that transforms $g$
to $\mathcal K(g)$. We also compute all automorphism orbits for each canonical
graphette. Thus, given any $k\le 8$ nodes in a graph $G$, we can in constant
time infer which graphette it is, as well as which orbit each of the $k$ nodes
belongs to. Sampling a large number $N$ of such $k$-sets of nodes provides an
approximation of both the distribution of graphlets and orbits across $G$, and
the orbit degree vector at each node.
