DegreeSketch：海量图的分布式基数草图及其应用
DegreeSketch: Distributed Cardinality Sketches on Massive Graphs with Applications
摘要：
我们提出度草图，一个半流式分布式草图数据结构，并证明其效用估计局部邻域大小和局部三角形计数重打击大规模图。DegreeSketch由分布在一组处理器中的以顶点为中心的基数草图组成，这些处理器在单次传递中积累，然后表现为能够近似回答与邻接集并集和交集的大小有关的图查询的持久查询引擎。顶点的第$t$个局部邻域是通过遍历最多$t$条边在$G$中从$v$可到达的顶点的数目，而局部三角形计数是包括它的3-循环的数目。这两个指标在图分析应用中都很有用，但随着图大小的增长，精确计算的规模很小。我们提出了有效的算法估计局部邻域大小和局部三角形计数沉重的打击使用DegreeSketch。在我们的实验中，我们使用著名的hyperloglog基数草图实现度草图，并利用分布式通信工具YGM实现最先进的性能在分布式存储器。
英文摘要：
We present DegreeSketch, a semi-streaming distributed sketch data structure and demonstrate its utility for estimating local neighborhood sizes and local triangle count heavy hitters on massive graphs. DegreeSketch consists of vertex-centric cardinality sketches distributed across a set of processors that are accumulated in a single pass, and then behaves as a persistent query engine capable of approximately answering graph queries pertaining to the sizes of adjacency set unions and intersections. The $t$th local neighborhood of a vertex is the number of vertices reachable in $G$ from $v$ by traversing at most $t$ edges, whereas the local triangle count is the number of 3-cycles in which it is included. Both metrics are useful in graph analysis applications, but exact computations scale poorly as graph sizes grow. We present efficient algorithms for estimating both local neighborhood sizes and local triangle count heavy hitters using DegreeSketch. In our experiments we implement DegreeSketch using the celebrated hyperloglog cardinality sketch and utilize the distributed communication tool YGM to achieve state-of-the-art performance in distributed memory.
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