  To control infection spreading on networks, we investigate the effect of
observer nodes that recognize infection in a neighboring node and make the rest
of the neighbor nodes immune. We numerically show that random placement of
observer nodes works better on networks with clustering than on locally
treelike networks, implying that our model is promising for realistic social
networks. The efficiency of several heuristic schemes for observer placement is
also examined for synthetic and empirical networks. In parallel with numerical
simulations of epidemic dynamics, we also show that the effect of observer
placement can be assessed by the size of the largest connected component of
networks remaining after removing observer nodes and links between their
neighboring nodes.
