Graph neural networks (GNNs) have been widely used under semi-supervised settings. Prior studies have mainly focused on finding appropriate graph filters (e.g., aggregation schemes) to generalize well for both homophilic and heterophilic graphs. Even though these approaches are essential and effective, they still suffer from the sparsity in initial node features inherent in the bag-of-words representation. Common in semi-supervised learning where the training samples often fail to cover the entire dimensions of graph filters (hyperplanes), this can precipitate over-fitting of specific dimensions in the first projection matrix. To deal with this problem, we suggest a simple and novel strategy; create additional space by flipping the initial features and hyperplane simultaneously. Training in both the original and in the flip space can provide precise updates of learnable parameters. To the best of our knowledge, this is the first attempt that effectively moderates the overfitting problem in GNN. Extensive experiments on real-world datasets demonstrate that the proposed technique improves the node classification accuracy up to 40.2 %

翻译：先前的研究主要侧重于寻找适当的图表过滤器(例如,汇总办法),以便全面推广同性图和异性相图。尽管这些方法至关重要,而且有效,但它们仍然在文字袋代表中固有的初始节点特征中受到过度的影响。在半监督学习中,培训样本往往无法覆盖图形过滤器(超平板)的全部层面,这种研究可能使第一个投影矩阵中的具体层面的配置过度。为了解决这一问题,我们建议了一个简单而新颖的战略;通过同时翻转初始特征和超高平面来创造更多的空间。原始和翻转空间的培训可以准确更新可学习的参数。据我们所知,这是第一次试图有效地缓解GNN中过于适应的问题。关于现实世界数据集的广泛实验表明,拟议的技术将节点分类精确度提高到40.2%。