Graphs are a prevalent tool in data science, as they model the inherent
structure of the data. They have been used successfully in unsupervised and
semi-supervised learning. Typically they are constructed either by connecting
nearest samples, or by learning them from data, solving an o
本文提出了一种新颖的流图半监督学习逼近方法，旨在捕捉标签分布的稀疏性并确保算法准确地传播标签，进一步将每个节点的空间复杂度降低到 O (1)，同时提供了适用于大型数据的分布式算法和为自然语言应用构建的图构建机制以及经过深度学习架构训练的鲁棒性图增强策略，实验结果证明该方法在内存占用上具有显著的降低，并且在性能上优于现有的最先进算法。