Joint Label Inference in Networks
Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy; 18(59):1−39, 2017.
We consider 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 people connected by a social network; by predicting these user profile fields, the network can provide a better experience to its users. Existing approaches such as Label Propagation (Zhu et al., 2003) fail to consider interactions between the label types. Our proposed method, called EDGEEXPLAIN explicitly models these interactions, while still allowing scalable inference under a distributed message- passing architecture. On a large subset of the Facebook social network, collected in a previous study (Chakrabarti et al., 2014), EDGEEXPLAIN outperforms label propagation for several label types, with lifts of up to $120\%$ for recall@1 and $60\%$ for recall@3.
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