斯坦福大学（StanfordUniversity）位于加利福尼亚州，临近旧金山，占地35平方公里，是美国面积第二大的大学。它被公认为世界上最杰出的大学之一，相比美国东部的常春藤盟校，特别是哈佛大学、耶鲁大学，斯坦福大学虽然历史较短，但无论是学术水准还是其他方面都能与常春藤名校相抗衡。斯坦福大学企业管理研究所和法学院在美国是数一数二的，美国最高法院的9个大法官，有6个是从斯坦福大学的法学院毕业的。
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【导读】近日斯坦福大学举办的《图学习》workshop，讲述了最新图机器学习进展，并讲述其在金融网络、自然语言处理、生物医学等方面的应用。
图作为一种抽象形式出现，用来表示复杂的数据，如社会网络、知识图谱、分子图、生物医学网络，以及用于建模3D对象、流形和源代码。图的机器学习，尤其是深度表示学习，是一个新兴的领域，从蛋白质折叠和欺诈检测到药物发现和推荐系统，有着广泛的应用。
在斯坦福图学习研讨会上，我们将汇集学术界和工业界的领袖，展示图神经网络最近的方法论进展。研讨会将展示领先的图机器学习框架和广泛的图机器学习在不同领域的应用。此外，研讨会将讨论大规模训练和部署基于图的机器学习模型的实际挑战。
目录内容：
0:44:42 Jure Leskovec, Stanford -- Welcome and Overview of Graph Representation Learning 图表示学习
1:12:19 Matthias Fey, TU Dortmund -- PyG 2.0: Advanced Representation Learning on Graphs 高级图表示学习
2:29:42 Industry panel - Andrew Zhai, Pinterest; Jaewon Yang, LinkedIn; Benedek Rozemberczki, AstraZeneca; Hatem Helal, Graphcore; Nadia Fawaz, Pinterest (moderator)
4:43:49 Jan Eric Lenssen, TU Dortmund -- Applications to Graphics and Vision
5:03:51 Rex Ying, Stanford -- Applications to Fraud and Intrusion Detection
5:25:50 Jiaxuan You, Stanford -- Applications to Financial Networks 图学习在金融神经网络应用
5:44:44 Hongyu Ren, Stanford -- Application to Knowledge Graphs 6:04:20 Antoine Bosselut, Stanford -- Applications in Natural Language Processing 自然语言处理应用
6:27:20 Maria Brbic, Stanford -- Applications in Biomedicine 生物医学应用
7:15:25 Jiaxuan You, Stanford -- GraphGym: Easy-to-use API for Graph Learning
7:35:20 Weihua Hu, Stanford -- Open Graph Benchmark: Large-Scale Challenge
7:59:13 Industry panel - Kim Branson, GlaxoSmithKline; Marinka Zitnik, Harvard University; Naren Chittar, JP Morgan Chase; Yu Liu, Facebook AI; Hema Raghavan, LinkedIn (moderator)
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【Jure Leskovec】Overview of Graph Representation Learning
Matthias Fey, TU Dortmund -- PyG 2.0: Advanced Representation Learning on Graphs
Rex Ying, Stanford -- Applications to Fraud and Intrusion Detection
Jiaxuan You, Stanford -- Applications to Financial Networks
Hongyu Ren, Stanford -- Application to Knowledge Graphs
Antoine Bosselut, Stanford -- Applications in Natural Language Processing
Maria Brbic, Stanford -- Applications in Biomedicine
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The impact of Universities on the social, economic and political landscape is one of the key directions in contemporary educational evaluation. In this paper, we discuss the new methodological technique that evaluates the impact of university based on popularity (number of page-views) of their alumni's pages on Wikipedia. It allows revealing the alumni popularity dynamics and tracking its state. Preliminary analysis shows that the number of page-views is higher for the contemporary persons that prove the perspectives of this approach. Then, universities were ranked based on the methodology and compared to the famous international university rankings ARWU and QS based only on alumni scales: for the top 10 universities, there is an intersection of two universities (Columbia University, Stanford University). The correlation coefficients between different university rankings are provided in the paper. Finally, the ranking based on the alumni popularity was compared with the ranking of universities based on the popularity of their webpages on Wikipedia: there is a strong connection between these indicators.
