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Traditional graph-based semi-supervised learning (SSL) approaches, eventhough widely applied, are not suited for massive data and large labelscenarios since they scale linearly with the number of edges $|E|$ and distinctlabels $m$. To deal with the large label size problem, recent works proposesketch-based methods to approximate the distribution on labels per node therebyachieving a space reduction from $O(m)$ to $O(\log m)$, under certainconditions. In this paper, we present a novel streaming graph-based SSLapproximation that captures the sparsity of the label distribution and ensuresthe algorithm propagates labels accurately, and further reduces the spacecomplexity per node to $O(1)$. We also provide a distributed version of thealgorithm that scales well to large data sizes. Experiments on real-worlddatasets demonstrate that the new method achieves better performance thanexisting state-of-the-art algorithms with significant reduction in memoryfootprint. We also study different graph construction mechanisms for naturallanguage applications and propose a robust graph augmentation strategy trainedusing state-of-the-art unsupervised deep learning architectures that yieldsfurther significant quality gains.

Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation