  Traditional graph-based semi-supervised learning (SSL) approaches, even
though widely applied, are not suited for massive data and large label
scenarios since they scale linearly with the number of edges $|E|$ and distinct
labels $m$. To deal with the large label size problem, recent works propose
sketch-based methods to approximate the distribution on labels per node thereby
achieving a space reduction from $O(m)$ to $O(\log m)$, under certain
conditions. In this paper, we present a novel streaming graph-based SSL
approximation that captures the sparsity of the label distribution and ensures
the algorithm propagates labels accurately, and further reduces the space
complexity per node to $O(1)$. We also provide a distributed version of the
algorithm that scales well to large data sizes. Experiments on real-world
datasets demonstrate that the new method achieves better performance than
existing state-of-the-art algorithms with significant reduction in memory
footprint. We also study different graph construction mechanisms for natural
language applications and propose a robust graph augmentation strategy trained
using state-of-the-art unsupervised deep learning architectures that yields
further significant quality gains.
