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

The explosive growth in fake news and its erosion to democracy, justice, and public trust has
increased the demand for fake news detection and intervention. This survey reviews and …

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 network (GNN)'s success in graph classification is closely related to the
Weisfeiler-Lehman (1-WL) algorithm. By iteratively aggregating neighboring node features …

Graph neural networks (GNNs) have been widely used in representation learning on graphs
and achieved state-of-the-art performance in tasks such as node classification and link …

Loosely-coupled and light-weight microservices running in containers are replacing
monolithic applications gradually. Understanding the characteristics of microservices is …

This book is a foundational guide to graph representation learning, including state-of-the art
advances, and introduces the highly successful graph neural network (GNN) formalism …

In recent years, graph neural networks (GNNs) have emerged as a powerful neural
architecture to learn vector representations of nodes and graphs in a supervised, end-to-end …

Abstract Recently, the Weisfeiler-Lehman (WL) graph isomorphism test was used to
measure the expressive power of graph neural networks (GNN). It was shown that the …

There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …