activity
20182026
most citedHandling Missing Data with Graph Representation Learning

94 citations · 265 across the 8 of their papers we have counts for

collaborators

14 papers

cs.LG20221 cited

AdaGrid: Adaptive Grid Search for Link Prediction Training Objective

Tim Poštuvan, Jiaxuan You, Mohammadreza Banaei +2

One of the most important factors that contribute to the success of a machine learning model is a good training objective. Training objective crucially influences the model's perfo…

cs.LG20217 cited

Identity-aware Graph Neural Networks

Jiaxuan You, Jonathan Gomes-Selman, Rex Ying +1

Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Wei…

cs.LG2020

Design Space for Graph Neural Networks

Jiaxuan You, Rex Ying, Jure Leskovec

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. However, current research focuses on proposing a…

cs.LG202094 cited

Handling Missing Data with Graph Representation Learning

Jiaxuan You, Xiaobai Ma, Daisy Yi Ding +2

Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and…

cs.AI2020

Inductive Learning on Commonsense Knowledge Graph Completion

Bin Wang, Guangtao Wang, Jing Huang +3

Commonsense knowledge graph (CKG) is a special type of knowledge graph (KG), where entities are composed of free-form text. However, most existing CKG completion methods focus on t…

cs.LG202022 cited

Graph Structure of Neural Networks

Jiaxuan You, Jure Leskovec, Kaiming He +1

Neural networks are often represented as graphs of connections between neurons. However, despite their wide use, there is currently little understanding of the relationship between…