… Here, we focus on pre-training as an approach to transfer learning in Graph Neural Networks
(GNNs) (Kipf & Welling, 2017; Hamilton et al., 2017a; Ying et al., 2018b; Xu et al., 2019; …

… We study deep weight-tied GNNs that have the parameter cost of only a single layer. We …
a deep graph equilibrium GNN, which is essentially a weight-tied network with infinite depth. …

… Thus, it is crucial to devise a self-supervised strategy to pre-train graph-level representations…
, a pretraining strategy for GNNs that learns to pre-train (L2P) at both node and graph levels …

… to help the optimization of deep neural networks. Curiously, … normalization is effective for
Graph Neural Networks (GNNs). … to the heavy batch noise in graph datasets. Second, we show …

… • Theoretically, we argue that the deep neural networks offer the advantage of being able to
capture non-linear information conveyed by the graph, whereas such information can not be …

… Graph Neural Networks (GNNs) are powerful deep learning models to generate node
embeddings on graphs. … Then in Section 4.4, we show how to extend our strategies to other graph …

… different types of deep learning methods on graphs. We divide … training strategies: graph
recurrent neural networks, graph … graphs, which are also referred to as graph neural networks (…

… There are potentially two types of model parameters in graph neural networks. f, … effectiveness,
we compare DistDGL’s graph partitioning algorithm with two alternatives: random graph …

… Recently, many studies on extending deep learning approaches for graph data have …
overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose …

… of distributed training of graph neural networks by investigating … ABSTRACT | Graph neural
networks (GNNs) have been … for their effectiveness in learning over graphs. To scale GNN …