图上的三元测度：楔形抽样的威力
Triadic Measures on Graphs: The Power of Wedge Sampling
摘要：
图用于在各种上下文中对交互进行建模，并且越来越需要快速评估图的结构。一些最有用的图表指标，特别是那些衡量社会凝聚力的指标，都是基于三角形的。尽管这些三元测量的重要性，相关的算法可能是非常昂贵的。我们提出了一种新的方法基于楔形抽样。这种多功能的技术允许快速和准确的近似所有当前变量的聚类系数，并使快速均匀采样的三角形的图形。我们的方法为所有计算提供了可证明和实用的时间近似权衡。我们提供了大量的结果，表明我们的方法比最先进的方法快了几个数量级，同时提供了几乎完全枚举的准确性。我们的研究结果将使更广泛地采用三元措施，分析非常大的图形，在几个现实世界的例子证明。
英文摘要：
Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of a graph. Some of the most useful graph metrics, especially those measuring social cohesion, are based on triangles. Despite the importance of these triadic measures, associated algorithms can be extremely expensive. We propose a new method based on wedge sampling. This versatile technique allows for the fast and accurate approximation of all current variants of clustering coefficients and enables rapid uniform sampling of the triangles of a graph. Our methods come with provable and practical time-approximation tradeoffs for all computations. We provide extensive results that show our methods are orders of magnitude faster than the state-of-the-art, while providing nearly the accuracy of full enumeration. Our results will enable more wide-scale adoption of triadic measures for analysis of extremely large graphs, as demonstrated on several real-world examples.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
