Multi-relational Weighted Tensor Decomposition

In real-world network data, there often exist multiple types of relationships (edges) that we would like to model. For instance, in social networks, relationships between individuals may be personal, familial, or professional. In this paper, we examine a multi-relational learning scenario in which the learner is given a small set of training examples, sampled from the complete set of potential pairwise relationships; the goal is then to perform transductive inference on the missing relationships. To do so, we present a tensor-based learning model, in which the relations may be either binaryor realvalued functions of the object pairs.