Joint Label Inference in Networks

Deepayan Chakrabarti, Stanislav Funiak, Jonathan Chang, Sofus A. Macskassy

Abstract: 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. Existing approaches such as Label Propagation 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, EdgeExplain outperforms label propagation for several label types, with lifts of up to $120%$ for [email protected] and 60% for [email protected]