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キーワード:
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要旨:
The data in many real-world problems can be thought of as a graph,
such as the web, co-author networks, and biological networks. We
propose a general regularization framework on graphs, which is
applicable to the classification, ranking, and link prediction
problems. We also show that the method can be explained as lazy
random walks. We evaluate the method on a number of experiments.
