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A distributed algorithm for graph semi-supervised learning
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DOI：10.1016/j.patrec.2021.07.021.png)
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Abstract
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Graph semi-supervised learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex optimization with Laplacian-based regularization. However, the current algorithm to solve the problem is centralized approach calling for heavy computational cost, particularly when the data is of large volume. In this paper, an innovate distributed algorithm is proposed to solve the problem, which is based on the decomposition of the similar graph. Contrary to the centralized approach, the distributed algorithm only requires the neighboring information for solving the optimization. It is proved that difference between the solutions of the distributed algorithm and centralized counterpart is upper bounded. We apply the proposed algorithm to both the synthetic and real-world datasets. The numerical results verify the effectiveness of the proposed distributed algorithm. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Graph semi-supervised learning (GSSL)
Graph signal processing
Laplacian
Distributed algorithm
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