Billion-node graphs pose signiﬁcant challenges at all levels from storage infrastructures to programming models. It is critical to develop a general purpose platform for graph processing. A distributed memory system is considered a feasible platform supporting online query processing as well as ofﬂine graph analytics. In this paper, we study the problem of partitioning a billion-node graph on such a platform, an important consideration because it has direct impact on load balancing and communication overhead. It is challenging not just because the graph is large, but because we can no longer assume that the data can be organized in arbitrary ways to maximize the performance of the partitioning algorithm. Instead, the algorithm must adopt the same data and programming model adopted by the system and other applications. In this paper, we propose a multi-level label propagation (MLP) method for graph partitioning. Experimental results show that our solution can partition billion-node graphs within several hours on a distributed memory system consisting of merely several machines, and the quality of the partitions produced by our approach is comparable to stateof-the-art approaches applied on toy-size graphs.