Learning missing edges via kernels in partially-known graphs

This paper deals with the problem of learning unknown edges with attributes in a partially-given multigraph. The method is an extension of Maximum Margin Multi-Valued Regression (MVM) to the case where those edges are characterized by different attributes. It is applied on a large-scale problem where an agent tries to learn unknown object-object relations by exploiting known such relations. The method can handle not only binary relations but also complex, structured relations such as text, images, collections of labels, categories, etc., which can be represented by kernels. We compare the performance with a specialized, state-of-the-art matrix completion method.