平衡图划分的优先级重流算法
Prioritized Restreaming Algorithms for Balanced Graph Partitioning
摘要：
平衡图划分是关系数据大规模分布式计算的关键步骤。随着图数据集的大小和密度的增长，一系列高度可扩展的平衡分区算法已经出现，以满足不同领域的各种需求。作为目前工作的起点，我们观察到最近引入的两个迭代分区家庭--基于重新流的和基于平衡标签传播的（包括Facebook的社交哈希分区）--可以通过设计决策的通用模块化框架来查看。在这种模块化视角的帮助下，我们发现设计决策的关键组合导致了一种新的算法家族，其经验性能明显优于任何现有的高度可扩展的算法在广泛的现实世界的图形。由此产生的优先级的再流算法采用的约束管理策略的基础上乘权重，借用再流文学，同时采用的概念，从平衡标签传播的优先级，以优化排序的流处理。我们的实验结果考虑了一系列的流顺序，其中动态排序的基础上，我们称之为矛盾的是广泛的最performative在切割质量的平衡分区，与静态排序的基础上度几乎一样好。
英文摘要：
Balanced graph partitioning is a critical step for many large-scale distributed computations with relational data. As graph datasets have grown in size and density, a range of highly-scalable balanced partitioning algorithms have appeared to meet varied demands across different domains. As the starting point for the present work, we observe that two recently introduced families of iterative partitioners---those based on restreaming and those based on balanced label propagation (including Facebook's Social Hash Partitioner)---can be viewed through a common modular framework of design decisions. With the help of this modular perspective, we find that a key combination of design decisions leads to a novel family of algorithms with notably better empirical performance than any existing highly-scalable algorithm on a broad range of real-world graphs. The resulting prioritized restreaming algorithms employ a constraint management strategy based on multiplicative weights, borrowed from the restreaming literature, while adopting notions of priority from balanced label propagation to optimize the ordering of the streaming process. Our experimental results consider a range of stream orders, where a dynamic ordering based on what we call ambivalence is broadly the most performative in terms of the cut quality of the resulting balanced partitions, with a static ordering based on degree being nearly as good.
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