Graph Data Augmentation

Local Augmentation

Introduced by Liu et al. in Local Augmentation for Graph Neural Networks

Local Augmentation for Graph Neural Networks, or LA-GNN, is a data augmentation technique that enhances node features by its local subgraph structures. Specifically, it learns the conditional distribution of the connected neighbors’ representations given the representation of the central node, which has an analogy with the Skip-gram of word2vec model that predicts the probability of the context given the central word. After augmenting the neighborhood, we concat the initial and the generated feature matrix as input for GNNs.

Source: Local Augmentation for Graph Neural Networks


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