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• W2133291805 abstract "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 hometown, current city, and employers, for users connected by a social network. Standard label propagation fails to consider the properties of the label types and the interactions between them. Our proposed method, called EdgeExplain, explicitly models these, while still enabling scalable inference under a distributed message-passing architecture. On a billion-node subset of the Facebook social network, EdgeExplain significantly outperforms label propagation for several label types, with lifts of up to 120% for recall@1 and 60% for recall@3." @default.
• W2133291805 created "2016-06-24" @default.
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• W2133291805 date "2014-01-30" @default.
• W2133291805 modified "2023-10-01" @default.
• W2133291805 title "Joint Inference of Multiple Label Types in Large Networks" @default.
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• W2133291805 hasPublicationYear "2014" @default.
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