Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …

In recent years, larger and deeper models are springing up and continuously pushing state-
of-the-art (SOTA) results across various fields like natural language processing (NLP) and …

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 …

Training Graph Neural Networks (GNNs) on large graphs is a fundamental challenge due to
the high memory usage, which is mainly occupied by activations (eg, node embeddings) …

Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research
and industry attention due to its high robustness and low computation and memory …

Distributed full-graph training of Graph Neural Networks (GNNs) over large graphs is
bandwidth-demanding and time-consuming. Frequent exchanges of node features …

Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph
convolution which can be realized by message passing between direct neighbors or …