We present the Temporal Graph Benchmark (TGB), a collection of challengi...
In recent years, algorithms and neural architectures based on the
Weisfe...
We present GNNAutoScale (GAS), a framework for scaling arbitrary
message...
In recent years, algorithms and neural architectures based on the
Weisfe...
Enabling effective and efficient machine learning (ML) over large-scale ...
We present a hierarchical neural message passing architecture for learni...
We present the Open Graph Benchmark (OGB), a diverse set of challenging ...
This work presents a generative adversarial architecture for generating
...
This work presents a two-stage neural architecture for learning and refi...
We propose a dynamic neighborhood aggregation (DNA) procedure guided by
...
We introduce PyTorch Geometric, a library for deep learning on irregular...
In recent years, graph neural networks (GNNs) have emerged as a powerful...
We present group equivariant capsule networks, a framework to introduce
...
The cuneiform script constitutes one of the earliest systems of writing ...
We present Spline-based Convolutional Neural Networks (SplineCNNs), a va...