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Fast distributed algorithms for testing graph properties

机译:用于测试图属性的快速分布式算法

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We initiate a thorough study of distributed property testingproducing algorithms for the approximation problems of property testing in the CONGEST model. In particular, for the so-called dense graph testing model we emulate sequential tests for nearly all graph properties having 1-sided tests, while in the general model we obtain faster tests for triangle-freeness and cycle-freeness, and in the sparse model we obtain a faster test for bipartiteness. In addition, we show a logarithmic lower bound for testing bipartiteness and cycle-freeness, which holds even in the stronger LOCAL model. In most cases, aided by parallelism, the distributed algorithms have a much shorter running time than their counterparts from the sequential querying model of traditional property testing. More importantly, the distributed algorithms we develop for testing graph properties are in many cases much faster than what is known for exactly deciding whether the property holds. The simplest property testing algorithms allow a relatively smooth transition to the distributed model. For the more complex tasks we develop new machinery that may be of independent interest.
机译:我们针对CONGEST模型中属性测试的近似问题,开始对分布式属性测试生成算法的深入研究。特别是,对于所谓的密集图测试模型,我们模拟了具有单面测试的几乎所有图属性的顺序测试,而在一般模型中,我们对稀疏模型的三角形无度和无周期性进行了更快的测试我们获得了更快的双向测试。此外,我们显示了用于测试二分性和无循环性的对数下限,即使在更强的LOCAL模型中也是如此。在大多数情况下,借助并行性,分布式算法的运行时间比传统属性测试的顺序查询模型中的算法要短得多。更重要的是,在很多情况下,我们开发的用于测试图形属性的分布式算法要比精确确定属性是否成立的算法快得多。最简单的属性测试算法允许相对平稳地过渡到分布式模型。对于更复杂的任务,我们开发了可能具有独立利益的新设备。

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