  Nowadays, in the big data era, social networks, graph databases, knowledge
graphs, electronic commerce etc. demand efficient and scalable capability to
process an ever increasing volume of graph-structured data. To meet the
challenge, two mainstream distributed programming models, vertex-centric (VC)
and subgraph-centric (SC) were proposed. Compared to the VC model, the SC model
converges faster with less communication overhead on well-partitioned graphs,
and is easy to program due to the "think like a graph" philosophy. The edge-cut
method is considered as a natural choice of subgraph-centric model for graph
partitioning, and has been adopted by Giraph++, Blogel and GRAPE. However, the
edge-cut method causes significant performance bottleneck for processing large
scale power-law graphs. Thus, the SC model is less competitive in practice. In
this paper, we present an innovative distributed graph computing framework,
DRONE (Distributed gRaph cOmputiNg Engine). It combines the subgraph-centric
model and the vertex-cut graph partitioning strategy. Experiments show that
DRONE outperforms the state-of-art distributed graph computing engines on
real-world graphs and synthetic power-law graphs. DRONE is capable of scaling
up to process one-trillion-edge synthetic power-law graphs, which is orders of
magnitude larger than previously reported by existing SC-based frameworks.
