Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model
in broad application fields for their effectiveness in learning over graphs. To scale GNN …

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and
learning from graph-structured data, with applications spanning numerous domains …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …

Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph
representation learning and have demonstrated superior performance in many real-world …

Temporal Graph Neural Networks (TGNNs) extend the success of Graph Neural Networks to
dynamic graphs. Distributed TGNN training requires efficiently tackling temporal …

Dynamic graphs play a crucial role in various real-world applications, such as link prediction
and node classification on social media and e-commerce platforms. Temporal Graph Neural …

Dynamic graph networks are widely used for learning time-evolving graphs, but prior work
on training these networks is inefficient due to communication overhead, long …

Dynamic Graph Neural Networks (DGNNs) have been widely applied in various real-life
applications, such as link prediction and pandemic forecast, to capture both static structural …

Graph neural networks (GNNs) suffer from low GPU utilization due to frequent memory
accesses. Existing concurrent training mechanisms cannot be directly adapted to GNNs …

Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …