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Lightweight Label Propagation for Large-Scale Network Data
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DOI：10.1109/TKDE.2019.2949297.png)
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摘要
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Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named SLP to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm is several times faster than state-of-the-art methods while achieving highly competitive performance.
Keyword:
Laplace equations
Loss measurement
Prototypes
Machine learning algorithms
Social networking (online)
Indexes
Scalability
Label propagation
scalability
stochastic
network data
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