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Applied Network Science
Fig. 5 | Applied Network Science

Fig. 5

From: The interplay between communities and homophily in semi-supervised classification using graph neural networks

Fig. 5

Feature propagation in original and \(Mix^+\) graphs. We visualize the embeddings of nodes in the first hidden layer of a trained GCN model for Cora-ML dataset projected to two dimensions with T-SNE. The embeddings in the original graph (on the left) show an overlap of the areas of different labels. The embeddings in the \(Mix^+\) graph (on the right) show less overlap and are more linearly separable already in the first layer. As a result, we see an increase of the performance with \(Mix^+\) networks

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