Jiaxuan You

GNNExplainer: Generating Explanations for Graph Neural Networks.

2019 · PubMed

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating …

Graph Structure of Neural Networks

2020 · arXiv (Cornell University)

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 the graph structure of the neural network and its predictive …

AdaGrid: Adaptive Grid Search for Link Prediction Training Objective

2022 · arXiv (Cornell University)

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 performance and generalization capabilities. This paper specifically focuses …

Hierarchical Graph Representation Learning with Differentiable Pooling

2018 · arXiv (Cornell University)

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods …

Handling Missing Data with Graph Representation Learning

2020 · arXiv (Cornell University)

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 label prediction where downstream labels are learned directly from …