Friend recommendation algorithms in large-scale social networks such as Facebook or Twitter usually require the exploration of huge user graphs. In current solutions for parallelizing graph algorithms, the burden of dealing with distributed concerns falls on algorithm developers. In this paper, a simple yet powerful programming interface (API) to implement distributed graph traversal algorithms is presented. A case study on implementing a followee recommendation algorithm for Twitter using the API is described. This case study not only illustrates the simplicity offered by the API for developing algorithms, but also how different aspects of the distributed solutions can be treated and experimented without altering the algorithm code. Experiments evaluating the performance of different job scheduling strategies illustrate the flexibility or our approach.