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Efficient Online Inference for Bayesian Nonparametric Relational Models
2013
Neural Information Processing Systems
Stochastic block models characterize observed network relationships via latent community memberships. In large social networks, we expect entities to participate in multiple communities, and the number of communities to grow with the network size. We introduce a new model for these phenomena, the hierarchical Dirichlet process relational model, which allows nodes to have mixed membership in an unbounded set of communities. To allow scalable learning, we derive an online stochastic variational
dblp:conf/nips/KimGBS13
fatcat:psk4chejvrgbnk7kp5pstxpymi