With graphs rapidly growing in size and deeper graph neural networks (GNNs) emerging,
the training and inference of GNNs become increasingly expensive. Existing network weight …

Graph representation learning serves as the core of important prediction tasks, ranging from
product recommendation to fraud detection. Real-life graphs usually have complex …

Brain graphs, which model the structural and functional relationships between brain regions,
are crucial in neuroscientific and clinical applications that can be formulated as graph …

The emergence of larger and deeper graph neural networks (GNNs) makes their training
and inference increasingly expensive. Existing GNN pruning methods simultaneously prune …

Due to the significant computational challenge of training large-scale graph neural networks
(GNNs), various sparse learning techniques have been exploited to reduce memory and …

Large-scale dynamic interaction graphs can be challenging to process and store, due to
their size and the continuous change of communication patterns between nodes. In this …

Graph neural networks (GNNs) have achieved great success on various tasks and fields that
require relational modeling. GNNs aggregate node features using the graph structure as …

Advances in computing resources have enabled the processing of vast amounts of data.
However, identifying trends in such data remains challenging for humans, especially in …

Node classification in temporal graphs aims to predict node labels based on historical
observations. In real-world applications, temporal graphs are complex with both graph …

Knowledge graphs (KGs), which store an extensive number of relational facts, serve various
applications. Recently, personalized knowledge graphs (PKGs) have emerged as a solution …