  In this paper, we propose algorithms that leverage a known community
structure to make group testing more efficient. We consider a population
organized in connected communities: each individual participates in one or more
communities, and the infection probability of each individual depends on the
communities (s)he participates in. Use cases include students who participate
in several classes, and workers who share common spaces. Group testing reduces
the number of tests needed to identify the infected individuals by pooling
diagnostic samples and testing them together. We show that making testing
algorithms aware of the community structure, can significantly reduce the
number of tests needed both for adaptive and non-adaptive group testing.
