Interpersonal relations facilitate information flow and give rise to positional advantage of individuals in a social network. We ask the question: How would an individual build relations with members of a dynamic social network in order to arrive at a central position in the network? We formalize this question using the dynamic network building problem. Two strategies stand out to solve this problem: The first directs the individual to exploit their social proximity by linking to nodes that are close-by, while the second tries its best to explore distant regions of the network. We evaluate and contrast these two strategies with respect to edge- and distance-based cost metrics, as well as other structural properties such as embeddedness and clustering coefficient. Experiments are performed on models of dynamic random graphs and real-world data sets. We then discuss and test ways that combine these two strategies.