不可错过！斯坦福《图学习》研讨会，Jure Leskovec主持，附slides！

在过去的几年里，图已经成为表示复杂数据的最重要和最有用的抽象之一，包括社交网络、知识图、金融交易/购买行为、供应链网络、分子图、生物医学网络，以及建模3D对象、流形和源代码。图上的深度表示学习是一个新兴的领域，具有广泛的应用，从蛋白质折叠和欺诈检测，到药物发现和推荐系统。

在斯坦福图学习研讨会上，我们将汇集来自学术界和工业界的思想领袖，展示图神经网络最前沿和最新的方法进展。研讨会将展示领先的图机器学习框架的新发展，以及不同领域的广泛的图机器学习应用。此外，研讨会还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。

Workshop Schedule

08:00 - 09:00   Registration & Breakfast

09:00 - 09:30   Jure Leskovec, Stanford University – Welcome and Overview of Graph Representation

09:30 - 10:00   Matthias Fey, PyG – What’s New in PyG

10:00 - 10:20   Ivaylo Bahtchevanov, PyG – Building PyG Open Source Community

10:20 - 10:40   Manan Shah & Dong Wang, Kumo.ai – Scaling-up PyG

10:40 - 11:00   Break

11:00 - 11:20   Rishi Puri, Nvidia – Accelerating PyG with Nvidia GPUs

11:20 - 11:40   Ke Ding, Intel – Accelerating PyG with Intel CPUs

11:40 - 12:00   Andreas Damianou, Spotify – Podcast Recommendations and Search Query Suggestions using GNNs at Spotify

12:00 - 12:20   Hema Raghavan & Tin-Yun Ho, Kumo.ai – Enabling Enterprises to Query the Future using PyG

12:20 - 12:30   Joseph Huang, Stanford University –** The Stanford CS LINXS Summer Research Program**

12:30 - 13:30   Lunch

13:30 - 13:50   Marinka Zitnik, Harvard University – Graph AI to Enable Precision Medicine

13:50 - 14:10   Bryan Perozzi, Google – Challenges and Solutions in Applying Graph Neural Networks at Google

14:10 - 14:30   Srijan Kumar, Georgia Institute of Technology – Dynamic and Signed GNNs for Web Safety and Integrity - Applications to Bad Actor Detection on Social Media Platforms

14:30 - 14:50   Luna Dong, Meta – Graph Mining for Next-Generation Intelligent Assistants on AR/VR Devices

14:50 - 15:10   Michi Yasunaga, Stanford University – Graph Learning in NLP Applications

15:10 - 15:30   Break

15:30 - 15:50   Weihua Hu, Stanford University – Open Graph Benchmark: Large-Scale Challenge

15:50 - 16:10   Hongyu Ren, Stanford University – Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs

16:10 - 17:00   Industry Panel – Challenges and Opportunities for Graph Learning