Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in
naturally modeling a broad range of systems where high-order relationships exist among …

Graph Neural Networks have attracted increasing attention in recent years. However,
existing GNN frameworks are deployed based upon simple graphs, which limits their …

Session-based recommendation (SBR) focuses on next-item prediction at a certain time
point. As user profiles are generally not available in this scenario, capturing the user intent …

A Gentle Introduction to Graph Neural Networks Distill About Prize Submit A Gentle Introduction
to Graph Neural Networks Neural networks have been adapted to leverage the structure and …

The emergence of large-scale pre-trained language models, such as ChatGPT, has
revolutionized various research fields in artificial intelligence. Transformersbased large …

The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …