In practice, identifying immunization targets may be impossible using such algorithms, because the structure of the contact network relevant for the spread of a directly transmissible disease is generally not known. Thus, algorithms that are agnostic about the full network structure are necessary to identify target individuals. The only algorithm we are aware of that is completely agnostic about the network structure network structure identifies target nodes by picking a random contact of a randomly chosen individual [42] . Once such an acquaintance has been picked n times, it is immunized. The acquaintance method has been shown to be able to identify some of the highly connected individuals, and thus approximates an immunization strategy that targets highly connected individuals. We propose an alternative algorithm (the so-called community bridge finder (CBF) algorithm, described in detail in the Methods) that aims to identify community bridges connecting two groups of clustered nodes. Briefly, starting from a random node, the algorithm follows a random path on the contact network, until it arrives at a node that does not connect back to more than one of the previously visited nodes on the random walk. The basic goal of the CBF algorithm is to find nodes that connect to multiple communities -it does so based on the notion that the first node that does not connect back to previously visited nodes of the current random walk is likely to be part of a different community. On all empirical and computationally generated networks tested, this algorithm performed mostly better, often equally well, and rarely worse than the alternative algorithm. It is important to note a crucial difference between algorithms such as CBF (henceforth called stochastic algorithms) and algorithms such as those that calculate, for example, the betweenness centrality of nodes (henceforth called deterministic algorithms). A deterministic algorithm always needs the complete information about each node (i.e. either the number or the identity of all connected nodes for each node in the network). A comparison between algorithms is therefore of limited use if they are not of the same type as they have to work with different inputs. Clearly, a deterministic algorithm with information on the full network structure as input should outperform a stochastic algorithm that is agnostic about the full network structure. Thus, we will restrict our comparison of CBF to the acquaintance method since this is the only stochastic algorithm we are aware of the takes as input the same limited amount of local information.