Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad
applicability to graph-related problems such as quantum chemistry, drug discovery, and high …

Semi-supervised node classification is a crucial challenge in relational data mining and has
attracted increasing interest in research on graph neural networks (GNNs). However …

Graph neural network (GNN) is a promising emerging application for link prediction,
recommendation, etc. Existing hardware innovation is limited to single-machine GNN (SM …

Graph Neural Networks (GNNs) have emerged as the state-of-the-art (SOTA) method for
graph-based learning tasks. However, it still remains prohibitively challenging to inference …

Convolutional Neural Networks (GCNs) have emerged as promising tools for graph-based
machine learning applications. Given that GCNs are both compute-and memory-intensive …

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad
applicability to graph-related problems such as quantum chemistry, drug discovery, and high …

Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs),
Graph Neural Networks (GNNs), and scientific computing applications have been proposed …

Graph convolutional neural networks (GCNs) have emerged as an effective approach to
extending deep learning for graph data analytics, but they are computationally challenging …

Dynamic graph neural network (DGNN) is becoming increasingly popular because of its
widespread use in capturing the dynamic features in the real world. A variety of dynamic …

The irregular adjacency matrix and the mismatched computation patterns of Aggregation
and Combination phases make Graph Neural Networks (GNNs) challenging to compute …