学习顶点分布未知的极大图
Learning Very Large Graphs with Unknown Vertex Distributions
摘要：
最近，Goldreich引入了具有未知分布的有界度图的属性测试的概念。我们建议对他的想法稍作修改：Radon-Nikodym神谕。使用这些预言机，任何合理的图形属性都可以在恒定时间内对平面图类别中的任何合理的未知分布进行测试。我们还讨论了随机局部分布算法，它适用于具有未知分布的非常大的图。最后，我们讨论了如何使用观察而不是采样来学习图形属性。
英文摘要：
Recently, Goldreich introduced the notion of property testing of bounded-degree graphs with an unknown distribution. We propose a slight modification of his idea: the Radon-Nikodym Oracles. Using these oracles any reasonable graph property can be tested in constant-time against any reasonable unknown distribution in the category of planar graphs. We also discuss Randomized Local Distributed Algorithms, which work on very large graphs with unknown distributions. Finally, we discuss how can we learn graph properties using observations instead of samplings.
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。
