The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …

With the help of deep learning, Graph Neural Networks (GNNs) have achieved remarkable
progress in various fields. However, due to the limitation of the message passing …

Reciprocal recommender systems (RRS) have been widely used in online platforms such as
online dating and recruitment. They can simultaneously fulfill the needs of both parties …

Graph neural networks (GNNs) have achieved great success in many scenarios with graph-
structured data. However, in many real applications, three issues arise when applying …

While Transformer-based pre-trained language models and their variants exhibit strong
semantic representation capabilities, the question of comprehending the information gain …

State-of-the-art predictive quality (PQ) applications use machine learning and deep learning
methods to learn patterns and classify a product's quality. Models typically estimate quality …

Attribute Network Embedding (ANE) is one of the most fundamental problems of graph
representation learning that was widely applied in numerous fields such as social …

Graph neural networks (GNNs) are powerful architectures for graph representation learning.
However, GNNs based on message-passing cannot make fully use of the valuable long …

The propagation of rumours on social media poses an important threat to societies, so that
various techniques for graph-based rumour detection have been proposed recently. Existing …