使用全局曲线球交易的大规模网络的并行和I/O高效随机化
Parallel and I/O-efficient Randomisation of Massive Networks using Global Curveball Trades
摘要：
图的随机化是网络分析和综合中的一项重要任务。它通常被实现为边切换过程（ESMC），在保持所涉及的度的同时重复地交换随机边对的节点。Curveball是一种新的方法，它考虑了随机抽取的节点对的整个邻域。它的马尔可夫链收敛到一个均匀分布，实验表明，它需要更少的步骤比建立ESMC。然而，由于交易成本更高，我们通过引入第一个高效的Curveball算法来研究Curveball的实际运行时间：用于简单无向图的I/O高效EM-CB及其内部内存悬挂IM-CB。此外，我们研究了在一个超级步骤中处理图中每个节点的全局交易，并表明无向全局交易收敛到均匀分布，并且在实践中表现出色。然后，我们讨论了全球贸易的EM-GCB和EM-PGCB，并给出实验证据，EM-PGCB实现了最先进的ESMC算法EM-ES的质量快近一个数量级。
英文摘要：
Graph randomisation is a crucial task in the analysis and synthesis of networks. It is typically implemented as an edge switching process (ESMC) repeatedly swapping the nodes of random edge pairs while maintaining the degrees involved. Curveball is a novel approach that instead considers the whole neighbourhoods of randomly drawn node pairs. Its Markov chain converges to a uniform distribution, and experiments suggest that it requires less steps than the established ESMC. Since trades however are more expensive, we study Curveball's practical runtime by introducing the first efficient Curveball algorithms: the I/O-efficient EM-CB for simple undirected graphs and its internal memory pendant IM-CB. Further, we investigate global trades processing every node in a graph during a single super step, and show that undirected global trades converge to a uniform distribution and perform superior in practice. We then discuss EM-GCB and EM-PGCB for global trades and give experimental evidence that EM-PGCB achieves the quality of the state-of-the-art ESMC algorithm EM-ES nearly one order of magnitude faster.
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