Title: Scalable Deep Generative Relational Models with High-Order Node Dependence Show Chinese title

Title: 具有高阶节点依赖的可扩展深度生成关系模型

Abstract: We propose a probabilistic framework for modelling and exploring the latent structure of relational data. Given feature information for the nodes in a network, the scalable deep generative relational model (SDREM) builds a deep network architecture that can approximate potential nonlinear mappings between nodes' feature information and the nodes' latent representations. Our contribution is two-fold: (1) We incorporate high-order neighbourhood structure information to generate the latent representations at each node, which vary smoothly over the network. (2) Due to the Dirichlet random variable structure of the latent representations, we introduce a novel data augmentation trick which permits efficient Gibbs sampling. The SDREM can be used for large sparse networks as its computational cost scales with the number of positive links. We demonstrate its competitive performance through improved link prediction performance on a range of real-world datasets. Show Chinese abstract

Abstract: 我们提出了一种概率框架，用于建模和探索关系数据的潜在结构。 给定网络中节点的特征信息，可扩展的深度生成关系模型（SDREM）构建了一个深度网络架构，可以近似节点特征信息与节点潜在表示之间的潜在非线性映射。 我们的贡献有两个方面：(1) 我们将高阶邻域结构信息纳入考虑，以在每个节点生成潜在表示，这些表示在网络中平滑变化。 (2) 由于潜在表示具有狄利克雷随机变量结构，我们引入了一种新颖的数据增强技巧，允许高效的吉布斯采样。 SDREM可用于大型稀疏网络，因为其计算成本与正链接的数量成比例。 我们通过在一系列真实世界数据集上的改进链接预测性能展示了其竞争力。