Many real data come in the form of non-grid objects, ie graphs, from social networks to
molecules. Adaptation of deep learning from grid-alike data (eg images) to graphs has …

Learning from graph and relational data plays a major role in many applications including
social network analysis, marketing, e-commerce, information retrieval, knowledge modeling …

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 …

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning
community in recent years. It has been widely adopted in various real-world applications …

Neural networks are typically designed to deal with data in tensor forms. In this paper, we
propose a novel neural network architecture accepting graphs of arbitrary structure. Given a …

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 …

Learning from graph and relational data plays a major role in many applications including
social network analysis, marketing, e-commerce, information retrieval, knowledge modeling …

Graphs have a superior ability to represent relational data, like chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

The last half-decade has seen a surge in deep learning research on irregular domains and
efforts to extend convolutional neural networks (CNNs) to work on irregularly structured data …