We propose another strategy, the community-bridge-finder (CBF) strategy, which rests on the observation that some individuals act as bridges between communities. The goal of the CBF algorithm is to identify such individuals based on random walks, without knowledge of the network structure, and thus without knowledge of the communities in a network. The algorithm works as follows: pick a random node v i = 0 and follow a random path (one node at a time, with the only condition that a The algorithm then picks two random neighbors of v 3 to check for connections to previously visited nodes -and finds one (to v 0 ). (e) Hence, v 2 is dismissed as a potential target, and the random walk continues to v 4 . Again, v 4 does not back-connect to any previously visited node (except, of course, to v 3 ), and thus v 3 is identified as a potential target -(f) thus again, two random neighboring nodes are picked to check for connections to previously visited nodes. Since no back connections can be found, v 3 is identified as a target and immunized. doi:10.1371/journal.pcbi.1000736.g007 node has not been visited by the random walk before). At every node v i$2 , check if there is more than one connection from v i to any of the visited nodes (the requirement for more than one connection stems from the simple fact that every node v i will have at least one connection to v i21 ). If there is just one back connection (i.e. from v i to v i21 ), a potential target v i21 has been identified. As an additional check, pick two random neighboring nodes of v i (other than v i21 ) and check for connections back to the previously visited nodes v j,i . If such connections exist, v i21 is not a potential target -continue the random walk at v i21 . If no such connections exist, immunize the potential target. Discard all information about visits, and start again at a randomly picked node v 0 . A schematic sketch of the algorithm is outlined in Figure 7 .