[HTML][HTML] Graph neural networks: A review of methods and applications
… of graph neural networks applied to structural scenarios, non-structural scenarios and other
scenarios. In Section 9, we propose four open problems of graph neural networks as well as …
scenarios. In Section 9, we propose four open problems of graph neural networks as well as …
A comprehensive survey on graph neural networks
… The complexity of graph data has imposed significant challenges on the … for graph data
have emerged. In this article, we provide a comprehensive overview of graph neural networks (…
have emerged. In this article, we provide a comprehensive overview of graph neural networks (…
How powerful are graph neural networks?
… , and financial networks, requires effective representation of their graph structure (Hamilton …
interest in Graph Neural Network (GNN) approaches for representation learning of graphs (Li …
interest in Graph Neural Network (GNN) approaches for representation learning of graphs (Li …
Graph neural networks: foundation, frontiers and applications
… range of topics in graph neural networks, by … -Graph Neural Networks (GNN):
Foundation, Frontiers and Applications [12], which can be easily accessed at https://graph-neural-networks.…
Foundation, Frontiers and Applications [12], which can be easily accessed at https://graph-neural-networks.…
Graph neural networks: Taxonomy, advances, and trends
… graph neural networks, and then refer to up to 327 relevant literatures to show the panorama
of the graph neural networks. … In order to drive the graph neural networks into a new stage, …
of the graph neural networks. … In order to drive the graph neural networks into a new stage, …
Towards deeper graph neural networks
… We develop a series of graph neural networks (GNNs) with different depths in terms of …
graph neural network with depth of 0, which is approximated with a multi-layer perceptron network…
graph neural network with depth of 0, which is approximated with a multi-layer perceptron network…
Benchmarking graph neural networks
In the last few years, graph neural networks (GNNs) have become the standard toolkit for
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …
analyzing and learning from data on graphs. This emerging field has witnessed an extensive …
Graph neural networks for social recommendation
… based on graph neural networks. Specially, we propose a novel graph neural network GraphRec
… • We propose a novel graph neural network GraphRec, which can model graph data in …
… • We propose a novel graph neural network GraphRec, which can model graph data in …
How neural networks extrapolate: From feedforward to graph neural networks
We study how neural networks trained by gradient descent extrapolate, ie, what they learn
outside the support of the training distribution. Previous works report mixed empirical results …
outside the support of the training distribution. Previous works report mixed empirical results …