动态图和超图的共享内存可伸缩k核维护

Kasimir Gabert, Ali Pinar, Ümit V. Çatalyürek
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引用次数: 12

摘要

计算图上的k核是一个重要的图挖掘目标,因为它提供了一种有效的方法来识别图的密集和内聚区域。在超图上计算k核最近引起了人们的兴趣,因为许多数据集自然会产生超图。当底层数据发生变化时,维护k-core非常重要,因为图很大、在增长,并且不断被修改。在许多实际应用中,图形更新是突然的,既有显著活动的时期,也有相对平静的时期。现有的维护算法无法处理大突发,并且先前的图和超图并行方法无法随着可用核的增加而扩展。我们通过提出两种并行和可扩展的全动态批处理算法来解决这些问题,这些算法用于在图和超图上维护k核。这两种算法都利用了k核和h索引之间的连接。一种算法适用于大批量,另一种算法适用于小批量。我们提供了第一个通过实验证明随着线程数量增加而可伸缩性的算法,同时在图和超图中保持高变化率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shared-Memory Scalable k-Core Maintenance on Dynamic Graphs and Hypergraphs
Computing k-cores on graphs is an important graph mining target as it provides an efficient means of identifying a graph’s dense and cohesive regions. Computing k-cores on hypergraphs has seen recent interest, as many datasets naturally produce hypergraphs. Maintaining k-cores as the underlying data changes is important as graphs are large, growing, and continuously modified. In many practical applications, the graph updates are bursty, both with periods of significant activity and periods of relative calm. Existing maintenance algorithms fail to handle large bursts, and prior parallel approaches on both graphs and hypergraphs fail to scale as available cores increase.We address these problems by presenting two parallel and scalable fully-dynamic batch algorithms for maintaining k-cores on both graphs and hypergraphs. Both algorithms take advantage of the connection between k-cores and h-indices. One algorithm is well suited for large batches and the other for small. We provide the first algorithms that experimentally demonstrate scalability as the number of threads increase while sustaining high change rates in graphs and hypergraphs.
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