Reciprocal and heterogeneous link prediction in social networks

Link prediction is a key technique in many applications in social networks, where potential links between entities need to be predicted. Conventional link prediction techniques deal with either homogeneous entities, e.g., people to people, item to item links, or non-reciprocal relationships, e.g., people to item links. However, a challenging problem in link prediction is that of heterogeneous and reciprocal link prediction, such as accurate prediction of matches on an online dating site, jobs or workers on employment websites, where the links are reciprocally determined by both entities that heterogeneously belong to disjoint groups. The nature and causes of interactions in these domains makes heterogeneous and reciprocal link prediction significantly different from the conventional version of the problem. In this work, we address these issues by proposing a novel learnable framework called ReHeLP, which learns heterogeneous and reciprocal knowledge from collaborative information and demonstrate its impact on link prediction. Evaluation on a large commercial online dating dataset shows the success of the proposed method and its promise for link prediction.