2015/12/07 by Bei Chen, Ning Chen, Chen, Bei +7
Computer Science · Mathematics · Physics and Astronomy · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1512.02016
Accepted by AAAI 2016
arxiv created 2015/12/07 · openalex publication_date 2015/12/07 · arxiv updated 2015/12/08 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. We further develop an approximate sampler using stochastic gradient Langevin dynamics to handle large networks with hundreds of thousands of entities and millions of links, orders of magnitude larger than what existing LFRM models can process. Extensive studies on various real networks show promising performance.