Chapter 15 Dynamic Graph Neural Networks

attention networks for undirected graphs. 326 Seyed Mehran Kazemi Graph Convolutional Networks: Graph convolutional networks (GCNs) (Kipf and Welling, 2017b) stack multiple layers of graph convolution. The l layer of GCN for an undirected graph G=(V,A,X) can be formulated as follows:

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  Network, Graph, Convolutional, Convolutional networks

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Chapter 12 Graph Neural Networks: Graph Transformation

graph-neural-networks.github.io

involves graphs in the domain of deep graph neural networks. First, the problem of graph transformation in the domain of graph neural networks are formalized in Section 12.1. Considering the entities that are being transformed during the trans-formation process, the graph transformation problem is further divided into four

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Chapter 5 The Expressive Power of Graph Neural Networks

graph-neural-networks.github.io

work, the message passing neural network, describing the limitations of its expres-sive power and discussing its efficient implementations. In Section 5.4, we will in-troduce a number of methods that make GNNs more powerful than the message passing neural network. In Section 5.5, we will conclude this chapter by discussing further research ...

  Network, Graph, Neural network, Neural, Graph neural

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Spatio-Temporal Graph Convolutional Networks (STGCN), to tackle the time series prediction prob-lem in trafÞc domain. Instead of applying regu-lar convolutional and recurrent units, we formulate the problem on graphs and build the model with complete convolutional structures, which enable much faster training speed with fewer parameters.

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KerGNNs: Interpretable Graph Neural Networks with Graph ...

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termed Kernel Graph Neural Networks (KerGNNs), which integrates graph kernels into the message passing process of GNNs. Inspired by convolution filters in convolutional neural networks (CNNs), KerGNNs adopt trainable hidden graphs as graph filters which are combined with subgraphs to update node embeddings using graph kernels. In addi-

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arXiv:1706.02216v4 [cs.SI] 10 Sep 2018

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Graph convolutional networks. In recent years, several convolutional neural network architectures for learning over graphs have been proposed (e.g., [4, 9, 8, 17, 24]). The majority of these methods do not scale to large graphs or are designed for whole-graph classification (or both) [4, 9, 8, 24].

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Chapter 12 Graph Neural Networks: Graph Transformation

graph-neural-networks.github.io

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