Analysis of Large Multi-Relational Networks
A multi-relational network (MRN) is a network with multiple types of edges. The analysis of MRNs, especially link prediction, has a wide range of applications such as building recommender systems, predicting protein-protein interactions, and automatic question answering. Because of the MRNs encountered in these applications are often very large, computationally efficient models are needed to synthesize information from multiple types of edges. In this talk, we will present a latent variable model for MRNs and discuss its statistical properties. We will also describe some methods to overcome the computational challenges of the model and propose a weighted negative sampling method to further improve the computational efficiency. The performance of the method will be demonstrated through a knowledge graph completion example.