Before revision:  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 .
After revision:  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 .