静态和动态图的共享内存并行最大团计数
Shared-Memory Parallel Maximal Clique Enumeration from Static and Dynamic Graphs
摘要：
最大团枚举（MCE）是一个基本的图挖掘问题，并作为一个原语在识别图中的密集结构是有用的。由于MCE的高计算成本，并行方法是必不可少的处理大型图。我们提出了MCE的共享内存并行算法，具有以下特性：（1）并行算法相对于最先进的顺序算法是可证明的工作高效的（2）算法具有可证明的小并行深度，表明它们可以扩展到大量处理器，以及（3）我们在多核机器上的实现随着核数量的增加而显示出良好的加速和扩展行为，并且比用于MCE的现有共享存储器并行算法快得多;例如，在某些输入图上，虽然先前的工作要么耗尽存储器，要么在5小时内完成，但我们的实现使用32个核心在一分钟内完成。我们还提出了工作效率的并行算法，用于维护一个动态图中的所有最大集团，是通过添加边的变化。
英文摘要：
Maximal Clique Enumeration (MCE) is a fundamental graph mining problem, and is useful as a primitive in identifying dense structures in a graph. Due to the high computational cost of MCE, parallel methods are imperative for dealing with large graphs. We present shared-memory parallel algorithms for MCE, with the following properties: (1) the parallel algorithms are provably work-efficient relative to a state-of-the-art sequential algorithm (2) the algorithms have a provably small parallel depth, showing they can scale to a large number of processors, and (3) our implementations on a multicore machine show good speedup and scaling behavior with increasing number of cores, and are substantially faster than prior shared-memory parallel algorithms for MCE; for instance, on certain input graphs, while prior works either ran out of memory or did not complete in 5 hours, our implementation finished within a minute using 32 cores. We also present work-efficient parallel algorithms for maintaining the set of all maximal cliques in a dynamic graph that is changing through the addition of edges.
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