Planetary gearboxes have various merits in mechanical transmission, but their complex
structure and intricate operation modes bring large challenges in terms of fault diagnosis …

Abstract Graph Neural Networks (GNNs) have become the de-facto standard tool for
modeling relational data. However, while many real-world graphs are directed, the majority …

Recently, graph neural networks (GNNs) have shown prominent performance in semi-
supervised node classification by leveraging knowledge from the graph database. However …

Predicting edge weights on graphs has various applications, from transportation systems to
social networks. This paper describes a Graph Neural Network (GNN) approach for edge …

The Graphical User Interface (GUI) plays a critical role in the interaction between users and
mobile applications (apps), aiming at facilitating the operation process. However, due to the …

Abstract Graph Convolutional Networks (GCNs) can be acknowledged as one of the most
significant methodologies for graph representation learning, and the family of GCNs has …

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and
spam, have severely disrupted the smooth operation of networks. Due to the inherent sender …

Link prediction models endeavor to understand the distribution of links within graphs and
forecast the presence of potential links. With the advancements in deep learning, prevailing …

Due to its capability to capture high-order information, the hypergraph model has shown
greater potential than the graph model in various scenarios. Real-world entity relations …

Abstract Graph autoencoders (Graph-AEs) learn representations of given graphs by aiming
to accurately reconstruct them. A notable application of Graph-AEs is graph-level anomaly …