Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Graph Neural Networks (GNNs) have evolved to understand graph structures through
recursive exchanges and aggregations among nodes. To enhance robustness, self …

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 …

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 …

Abstract Recent studies on Graph Neural Networks (GNNs) provide both empirical and
theoretical evidence supporting their effectiveness in capturing structural patterns on both …

Graph neural networks (GNNs) have emerged as a series of competent graph learning
methods for diverse real-world scenarios, ranging from daily applications like …

Graph Neural Networks (GNNs) have shown great power in learning node representations
on graphs. However, they may inherit historical prejudices from training data, leading to …

Conventional graph neural networks (GNNs) are often confronted with fairness issues that
may stem from their input, including node attributes and neighbors surrounding a node …

Abstract Graph Neural Network (GNN) models have been extensively researched and
utilised for extracting valuable insights from graph data. The performance of fairness …