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**1 - 2**of**2**### Large Scale Distributed Semi-Supervised Learning Using Streaming Approximation

"... Abstract Traditional graph-based semi-supervised learning (SSL) approaches 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 ..."

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Abstract Traditional graph-based semi-supervised learning (SSL) approaches 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 label distribution 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 graphbased SSL approximation that effectively captures the sparsity of the label distribution 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. Finally, we propose a robust graph augmentation strategy using unsupervised deep learning architectures that yields further significant quality gains for SSL in natural language applications.

### Joint Inference of Multiple Label Types in Large Networks

"... We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as home-town, curr ..."

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We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as home-town, current city, and employers, for users con-nected by a social network. Standard label prop-agation fails to consider the properties of the la-bel types and the interactions between them. Our proposed method, called EDGEEXPLAIN, explic-itly models these, while still enabling scalable inference under a distributed message-passing architecture. On a billion-node subset of the Facebook social network, EDGEEXPLAIN signif-icantly outperforms label propagation for several label types, with lifts of up to 120 % for recall@1 and 60 % for recall@3. 1.