We introduce the nonparametric metadata dependent relational (NMDR) model, a
Bayesian nonparametric stochastic block model for network data. The NMDR allows
the entities associated with each node to have mixed membership in an unbounded
collection of latent communities. Learned regression models allow these
memberships to depend on, and be predicted from, arbitrary node metadata. We
develop efficient MCMC algorithms for learning NMDR models from partially
observed node relationships. Retrospective MCMC methods allow our sampler to
work directly with the infinite stick-breaking representation of the NMDR,
avoiding the need for finite truncations. Our results demonstrate recovery of
useful latent communities from real-world social and ecological networks, and
the usefulness of metadata in link prediction tasks.

介绍非参数元数据依赖关系（NMDR）模型，一种贝叶斯非参数随机块模型用于网络数据，通过学习回归模型，使每个节点上的实体可以在无限多个潜在社区中具有混合成员资格，并且可以依赖和预测任意节点元数据。使用有效的 MCMC 算法从部分观察的节点关系中学习 NMDR 模型，回顾性 MCMC 方法可以直接使用 NMDR 的无限粘性分解表示，避免了有限截断的需要。结果表明，从真实的社交和生态网络中恢复出有用的潜在社区，元数据在链接预测任务中也是有用的。