  The pair-matching problem appears in many applications where one wants to
discover good matches between pairs of entities or individuals. Formally, the
set of individuals is represented by the nodes of a graph where the edges,
unobserved at first, represent the good matches. The algorithm queries pairs of
nodes and observes the presence/absence of edges. Its goal is to discover as
many edges as possible with a fixed budget of queries. Pair-matching is a
particular instance of multi-armed bandit problem in which the arms are pairs
of individuals and the rewards are edges linking these pairs. This bandit
problem is non-standard though, as each arm can only be played once.
  Given this last constraint, sublinear regret can be expected only if the
graph presents some underlying structure. This paper shows that sublinear
regret is achievable in the case where the graph is generated according to a
Stochastic Block Model (SBM) with two communities. Optimal regret bounds are
computed for this pair-matching problem. They exhibit a phase transition
related to the Kesten-Stigum threshold for community detection in SBM. The
pair-matching problem is considered in the case where each node is constrained
to be sampled less than a given amount of times. We show how optimal regret
rates depend on this constraint. The paper is concluded by a conjecture
regarding the optimal regret when the number of communities is larger than 2.
Contrary to the two communities case, we argue that a statistical-computational
gap would appear in this problem.
