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StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs

机译:流 - 一种基于流的动态图表中的基于流的算法

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Determining the occurrence of motifs yields profound insight for many biological systems, like metabolic, protein-protein interaction, and protein structure networks. Meaningful spatial protein-structure motifs include enzyme active sites and ligand-binding sites which are essential for function, shape, and performance of an enzyme. Analyzing their dynamics over time leads to a better understanding of underlying properties and processes. In this work, we present StreaM, a stream-based algorithm for counting undirected 4-vertex motifs in dynamic graphs. We evaluate StreaM against the four predominant approaches from the current state of the art on generated and real-world datasets, a simulation of a highly dynamic enzyme. For this case, we show that StreaM is capable to capture essential molecular protein dynamics and thereby provides a powerful method for evaluating large molecular dynamics trajectories. Compared to related work, our approach achieves speedups of up to 2,300 times on real-world datasets.
机译:确定基序的发生产生许多生物系统的深刻洞察力,例如代谢,蛋白质 - 蛋白质相互作用和蛋白质结构网络。有意义的空间蛋白质结构基序包括酶活性位点和配体结合位点,这对于酶的功能,形状和性能至关重要。随着时间的推移分析他们的动态导致更好地了解潜在的属性和流程。在这项工作中,我们呈现了一种基于流的算法,用于在动态图中计算无向4 - 顶点图案。我们评估对来自生成和现实世界数据集的当前现有技术的四种主要方法的流,是一种高动态酶的模拟。对于这种情况,我们表明,流能够捕捉到重要的分子蛋白质动力学,从而为评估大分子动力学轨迹的有效方法。与相关工作相比,我们的方法在现实世界数据集中实现了高达2,300次的加速。

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