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Graph neural networks
Graphs are flexible mathematical objects that can represent many entities and knowledge
from different domains, including in the life sciences. Graph neural networks (GNNs) are …
from different domains, including in the life sciences. Graph neural networks (GNNs) are …
Graph neural networks for materials science and chemistry
Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …
and materials science, being used to predict materials properties, accelerate simulations …
Exploring the potential of large language models (llms) in learning on graphs
Learning on Graphs has attracted immense attention due to its wide real-world applications.
The most popular pipeline for learning on graphs with textual node attributes primarily relies …
The most popular pipeline for learning on graphs with textual node attributes primarily relies …
Low-latency automotive vision with event cameras
The computer vision algorithms used currently in advanced driver assistance systems rely
on image-based RGB cameras, leading to a critical bandwidth–latency trade-off for …
on image-based RGB cameras, leading to a critical bandwidth–latency trade-off for …
SE (3) diffusion model with application to protein backbone generation
The design of novel protein structures remains a challenge in protein engineering for
applications across biomedicine and chemistry. In this line of work, a diffusion model over …
applications across biomedicine and chemistry. In this line of work, a diffusion model over …
Recipe for a general, powerful, scalable graph transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …
with linear complexity and state-of-the-art results on a diverse set of benchmarks. Graph …
Revisiting heterophily for graph neural networks
Abstract Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using
graph structures based on the relational inductive bias (homophily assumption). While …
graph structures based on the relational inductive bias (homophily assumption). While …
Geometry-informed neural operator for large-scale 3d pdes
We propose the geometry-informed neural operator (GINO), a highly efficient approach for
learning the solution operator of large-scale partial differential equations with varying …
learning the solution operator of large-scale partial differential equations with varying …
Long range graph benchmark
Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …
paradigm generally exchange information between 1-hop neighbors to build node …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …