大规模图数据划分算法综述

Summary of Large-Scale Graph Partitioning Algorithms

对大规模图数据划分算法进行了总结,介绍了并行环境下图计算模型,详述了大规模静态图划分算法和动态图划分算法,归纳了这些算法的优缺点以及适应性。最后,指出了关于大图划分尚未探索的有意义的研究课题。

The large-scale graph partitioning algorithms were summarized and graph computing models in the distributed environment were introduced. Firstly the large-scale static graph partitioning algorithms and the dynamic graph partitioning algorithms were discussed. Then the advantages and disadvantages of these algorithms and its adaptability conscientiously were sumed up. Finally, some meaningful research subjects about the distributed graph partition, which have not been explored were pointed out.