Routing information through networks is a universal phenomenon in both natural and manmade complex systems. When each node has full knowledge of the global network connectivity, finding short communication paths is merely a matter of distributed computation. However, in many real networks nodes communicate efficiently even without such global intelligence. We have shown that latent network geometry can guide the routing process, leading to efficient communication without global knowledge of the network structure in arbitrarily large networks as soon as their structure is similar to the structure of many large real networks. Therefore neuronal firing in the brain, signaling pathways in gene-regulatory networks, routing in the Internet, and search patterns in social networks may all be driven by properties of latent geometric spaces underlying these systems. Understanding communication patterns in neural and social networks as well as the development of efficient communication protocols in technological systems are of keen interest to our lab.