103 papers with code • 0 benchmarks • 3 datasets
In this work, we remove the restriction of using only the direct neighbors by introducing a powerful, yet spatially localized graph convolution: Graph diffusion convolution (GDC).
Ranked #1 on Node Classification on AMZ Comp
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #23 on Node Classification on Cora
We show that the expressive models significantly outperform simple scalable baselines, indicating an opportunity for dedicated efforts to further improve graph ML at scale.
Ranked #1 on Node Classification on MAG240M-LSC
To address this, we propose Graph Agreement Models (GAM), which introduces an auxiliary model that predicts the probability of two nodes sharing the same label as a learned function of their features.
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering.
Ranked #5 on Image Classification on iNaturalist
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.