  Graph neural networks (GNNs) are commonly used in semi-supervised settings.
Previous research has primarily focused on finding appropriate graph filters
(e.g. aggregation methods) to perform well on both homophilic and heterophilic
graphs. While these methods are effective, they can still suffer from the
sparsity of node features, where the initial data contain few non-zero
elements. This can lead to overfitting in certain dimensions in the first
projection matrix, as training samples may not cover the entire range of graph
filters (hyperplanes). To address this, we propose a novel data augmentation
strategy. Specifically, by flipping both the initial features and hyperplane,
we create additional space for training, which leads to more precise updates of
the learnable parameters and improved robustness for unseen features during
inference. To the best of our knowledge, this is the first attempt to mitigate
the overfitting caused by the initial features. Extensive experiments on
real-world datasets show that our proposed technique increases node
classification accuracy by up to 46.5% relatively.
