Adaptive Group Testing on Networks with Community Structure

Since the inception of the group testing problem in World War II, the prevailing assumption in the probabilistic variant of the problem has been that individuals in the population are infected by a disease independently. However, this assumption rarely holds in practice, as diseases typically spread through connections between individuals. We introduce an infection model for networks, inspired by characteristics of COVID-19 and similar diseases, which generalizes the traditional i.i.d. model from probabilistic group testing. Under this infection model, we ask whether knowledge of the network structure can be leveraged to perform group testing more efficiently, focusing specifically on community-structured graphs drawn from the stochastic block model. Through both theory and simulations, we show that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes. Finally, we derive novel information-theoretic lower bounds which highlight the fundamental limits of adaptive group testing in our networked setting.