The　bulk　synchronous　parallel　(BSP)　model　is　very　user　friendly　for　coding　and　debugging　parallel　graph　algorithms.　However,　existing　BSP-based　distributed　graph-processing　frameworks,　such　as　Pregel,　GPS　and　Giraph,　routinely　suffer　from　high　communication　costs.　These　high　communication　costs　mainly　stem　from　the　fine-grained　message-passing　communication　model.　In　order　to　address　this　problem,　we　propose　a　new　computation　model　with　low　communication　costs,　called　LCC-BSP.　We　use　this　model　to　design　and　implement　a　high-performance　distributed　graph-processing　framework　called　LCC-Graph.　This　framework　eliminates　high　communication　costs　in　existing　distributed　graph-processing　frameworks.　Moreover,　LCC-Graph　also　balances　the　computation　workloads　among　all　compute　nodes　by　optimizing　graph　partitioning,　significantly　reducing　the　computation　time　for　each　superstep.　Evaluation　of　LCC-Graph　on　a　32-node　cluster,　driven　by　real-world　graph　datasets,　shows　that　it　significantly　outperforms　existing　distributed　graph-processing　frameworks　in　terms　of　run-time,　particularly　when　the　system　is　supported　by　a　high-bandwidth　network.　For　example,　LCC-Graph　achieves　an　order　of　magnitude　performance　improvement　over　GPS　and　GraphLab.