GNN的发展
- Spectral graph theory: The first prominent research on GCNs is presented in Bruna et al. (2013), which develops a variant of graph convolution based on spectral graph theory
- Since that time, there have been increasing improvements, extensions, and approximations on spectral-based graph convolutional networks
- Spatial-based graph convolutional networks: As spectral methods usually handle the whole graph simultaneously and are difficult to parallel or scale to large graphs, spatial-based graph convolutional networks have rapidly developed recently
- Together with sampling strategies, the computation can be performed in a batch of nodes instead of the whole graph [24], [27], which has the potential to improve the efficiency.
- Others: In addition to graph convolutional networks, many alternative graph neural networks have been developed in the past few years.
- These approaches include graph attention networks, graph autoencoders, graph generative networks, and graph spatial-temporal networks.
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