In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

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 …

Graph neural network (GNN) has been widely applied in real-world applications, such as
product recommendation in e-commerce platforms and risk control in financial management …

Training and inference with graph neural networks (GNNs) on massive graphs in a
distributed environment has been actively studied since the inception of GNNs, owing to the …

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 …

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

The widespread use of Sparse Matrix Dense Matrix Multiplication (SpMM) and Sampled
Dense Matrix Dense Matrix Multiplication (SDDMM) kernels makes them candidates for …

Physics-informed machine learning (PIML) has emerged as a promising new approach for
simulating complex physical and biological systems that are governed by complex …

Spectral Clustering (SC) has been the main subject of intensive research due to its
remarkable clustering performance. Despite its successes, most existing SC methods suffer …