In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Deep learning has seen significant growth recently and is now applied to a wide range of
conventional use cases, including graphs. Graph data provides relational information …

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 …

Graphs are useful data structures in complex real-life applications such as modeling
physical systems, learning molecular fingerprints, controlling traffic networks, and …

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making
use of graph structure based on the relational inductive bias (edge bias), rather than treating …

Inspired by the extensive success of deep learning, graph neural networks (GNNs) have
been proposed to learn expressive node representations and demonstrated promising …

Graph neural networks (GNNs) are effective machine learning models for various graph
learning problems. Despite their empirical successes, the theoretical limitations of GNNs …

Graphs are ubiquitous in nature and can therefore serve as models for many practical but
also theoretical problems. For this purpose, they can be defined as many different types …