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https://gm-neurips-2020.github.io/
在这次演讲中，Graph Mining team的创始人Vahab对本图挖掘和学习进行了高层次的介绍。这个演讲涉及到什么是图，为什么它们是重要的，以及它们在大数据世界中的位置。然后讨论了组成图挖掘和学习工具箱的核心工具，并列出了几个规范的用例。它还讨论了如何结合算法、系统和机器学习来在不同的分布式环境中构建一个可扩展的图学习系统。最后，它提供了关于Google一个简短的历史图挖掘和学习项目。本次演讲将介绍接下来的演讲中常见的术语和主题。
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【312页ppt】Mining and Learning with Graphs at Scale
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An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially private analytics: Google DP, SmartNoise, diffprivlib, diffpriv, and Chorus. We compare these libraries qualitatively (capabilities, features, and maturity) and quantitatively (utility and scalability) across four analytics queries (count, sum, mean, and variance) executed on synthetic and real-world datasets. We conclude that these libraries provide similar utility (except in some notable scenarios). However, there are significant differences in the features provided, and we find that no single library excels in all areas. Based on our results, we provide guidance for practitioners to help in choosing a suitable library, guidance for library designers to enhance their software, and guidance for researchers on open challenges in differential privacy tools for non-experts.
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