Doorkeeper

High-dimensional Statistical Modeling Team Seminar (Talk by Mr. Weihua Hu, Stanford University)

Thu, 02 Sep 2021 10:00 - 11:00 JST
Online Link visible to participants
Register

Registration is closed

Get invited to future events

Free admission
-Time Zone:JST -The seats are available on a first-come-first-served basis. -When the seats are fully booked, we may stop accepting applications. -Simultaneous interpretation will not be available.

Description

Title: Open Graph Benchmark Large-Scale Challenge

Abstract:
We first present the Open Graph Benchmark (OGB), a diverse set of challenging and realistic benchmark datasets to facilitate scalable, robust, and reproducible graph machine learning (ML) research. OGB datasets are larger than existing graph benchmarks, encompass multiple important graph ML tasks, and cover a diverse range of domains. We then present OGB’s new initiative on a Large-Scale Challenge (OGB-LSC) at the KDD Cup 2021. OGB-LSC provides datasets that represent modern industrial-scale large graphs. We provide dedicated baseline experiments, scaling up expressive graph ML models to the massive datasets. We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.

Bio:
Weihua Hu is a Ph.D. student of Computer Science at Stanford University, advised by Jure Leskovec. His research interests lie in graph representation learning and its applications to scientific discovery. His recent research is on advancing the field of Graph Neural Networks, by improving their theoretical understanding and generalization capability as well as building large-scale datasets for benchmarking models. He also actively applies his research to drug discovery and material discovery. He is supported by Funai Overseas Scholarship and Masason Foundation Fellowship. Before joining Stanford, Weihua received his Bachelor's and Master’s degrees both from the University of Tokyo, where he received the best Master’s thesis award.

About this community

RIKEN AIP Public

RIKEN AIP Public

Public events of RIKEN Center for Advanced Intelligence Project (AIP)

Join community