Graph neural networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Nowadays, the analysis of complex phenomena modeled by graphs plays a crucial role in
many real-world application domains where decisions can have a strong societal impact …

Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …

Large pre-trained vision-language models (VLMs) reduce the time for developing predictive
models for various vision-grounded language downstream tasks by providing rich …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

Graph Neural Networks (GNNs) have become increasingly important due to their
representational power and state-of-the-art predictive performance on many fundamental …

The emergence of Graph Neural Networks (GNNs) has greatly advanced the development
of recommendation systems. Recently, many researchers have leveraged GNN-based …

A plethora of fair graph neural networks (GNNs) have been proposed to promote fairness in
models for high-stake real-life contexts. Meanwhile, explainability is generally proposed to …

Tackling unfairness in graph learning models is a challenging task, as the unfairness issues
on graphs involve both attributes and topological structures. Existing work on fair graph …

To date, graph collaborative filtering (CF) strategies have been shown to outperform pure CF
models in generating accurate recommendations. Nevertheless, recent works have raised …