There has recently been significant interest in applications that require computations on massive graph structures, including scenarios where the graph is too large to be processed on a single machine. In this case, the graph needs to be partitioned into subgraphs that can be assigned to individual machines, in a process called graph or social network sharding. Given the sizes of the graphs involved, it is necessary or at least highly desirable that the partitioning itself can also be performed in a distributed manner, instead of running a sequential partitioning algorithm on a single node.We study such distributed algorithms for graph sharding, where the goal is to create subgraphs of roughly equal size that minimize the number of edges crossing subgraph boundaries. In particular, we focus on two well-known approaches that can be efficiently i mplemented i n M apReduce a nd r elated distributed computing paradigms: the Balanced Label Propagation algorithm of Ugander and Backstrom, and the method of Duong et al. based on the Bayesian Stochastic Block Modeling approach of Hofman and Wiggins. Our contributions are as follows: (1) We perform the first direct experimental comparison of the two approaches, which were independently proposed and published. (2) We propose and evaluate several enhancements of Balanced Label Propagation that result in improved graph shardings. (3) We propose and evaluate hybrid methods that perform label propagation both on individual nodes, as suggested by Ugander and Backstrom, and on stochastic blocks inferred using the approach of Duong et al.