4 Graph Neural Networks you Need to Know (WLG, GCN, GAT, GIN)

Author(s): Edward Ma

Originally published on Towards AI.

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We went through Knowledge Graph Embeddings and Random Walk in previous graph neural network stories. Knowledge graph embeddings train entity embeddings for downstream tasks. On the other hand, several neural networks model apply random walk theory to train entity embeddings.

In this story, we will go focus on 4 graph neural network models which are Weisfeiler-Lehman Graph Kernel (Shervashidze et al., 2011), Graph Convolutional Network (Kipf and Welling, 2017), Graph Attention Networks (Veličković et al., 2017) and Graph Isomorphism Network (Xu et al., 2019)

Shervashidze et al. (2011) introduce a way to measure graph similarity (WL… Read the full blog for free on Medium.

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Published via Towards AI