Methods › Graphs › Graph Data Augmentation › Local Augmentation
Local Augmentation
Introduced by Songtao Liu et al. in Local Augmentation for Graph Neural Networks
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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.
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