Scalable Deep Generative Relational Models with High-Order Node Dependence
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Data AugmentationLink PredictionVocal Bursts Intensity PredictionSimilar Papers 제목 키워드 기반
Scalable Deep Generative Relational Model with High-Order Node Dependence
In this work, we propose a probabilistic framework for relational data modelling and latent structure exploring. Given the possible feature information for the nodes in a network, our model builds up a deep architecture …
Data AugmentationLink PredictionVocal Bursts Intensity PredictionDeep Amortized Relational Model with Group-Wise Hierarchical Generative Process
In this paper, we propose Deep amortized Relational Model (DaRM) with group-wise hierarchical generative process for community discovery and link prediction on relational data (e.g., graph, network). It provides an effic…
Community DetectionLink PredictionGenerative hypergraph clustering: from blockmodels to modularity
Hypergraphs are a natural modeling paradigm for a wide range of complex relational systems. A standard analysis task is to identify clusters of closely related or densely interconnected nodes. Many graph algorithms for t…
ClusteringCommunity DetectionGraph ClusteringPropagation on Multi-relational Graphs for Node Regression
Recent years have witnessed a rise in real-world data captured with rich structural information that can be conveniently depicted by multi-relational graphs. While inference of continuous node features across a simple gr…
Node RegressionregressionRelational ReasoningHierarchical Attention Models for Multi-Relational Graphs
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relational graph convolutions, to effectively ope…
Graph AttentionLink PredictionNode ClassificationRelation