Relational Deep Learning: A Deep Latent Variable Model for Link Prediction
Explore this paper's citation graph
Summary
This work follows the Bayesian deep learning framework and devise a hierarchical Bayesian model, called relational deep learning (RDL), to jointly model high-dimensional node attributes and link structures with layers of latent variables and derive a generalized variational inference algorithm for learning the variables and predicting the links.
- Type
- article
- Published
- 2017-02-04
- Cited by
- 63
- References
- 31
- Access
- Open access
- OpenAlex
- https://openalex.org/W2604682274
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:19416217
Keywords
Computer science, Inference, Latent variable, Artificial intelligence, Statistical relational learning
References
- Relational Stacked Denoising Autoencoder for Tag Recommendation
- Pattern Recognition and Machine Learning
- Auto-Encoding Variational Bayes
- A Survey of Statistical Network Models
- Discriminative Relational Topic Models
- Latent Space Approaches to Social Network Analysis
- Mixed Membership Stochastic Blockmodels
- Relational learning via collective matrix factorization
- Hierarchical relational models for document networks
- Link Prediction in Relational Data
- Collaborative topic modeling for recommending scientific articles
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
- Deep Recursive Neural Networks for Compositionality in Language
- DeepWalk: online learning of social representations
- Collaborative Deep Learning for Recommender Systems
- ImageNet classification with deep convolutional neural networks
- Dynamic Egocentric Models for Citation Networks
- Visualizing Data using t-SNE
- Relation Classification via Convolutional Deep Neural Network
- Structural Deep Network Embedding
Cited by
- Towards Bayesian Deep Learning: A Survey
- Learning Edge Representations via Low-Rank Asymmetric Projections
- Sparse Relational Topical Coding on multi-modal data
- Relational Variational Autoencoder for Link Prediction with Multimedia Data
- RelNN: A Deep Neural Model for Relational Learning
- Neural Ideal Point Estimation Network
- Graphite: Iterative Generative Modeling of Graphs
- Neural Relational Topic Models for Scientific Article Analysis
- Supervised User Ranking in Signed Social Networks
- Linked Variational AutoEncoders for Inferring Substitutable and Supplementary Items
- Model-free inference of diffusion networks using RKHS embeddings
- Bidirectional Inference Networks: A Class of Deep Bayesian Networks for Health Profiling
- Neural Tensor Factorization for Temporal Interaction Learning
- Bayesian deep learning for integrated intelligence : bridging the gap between perception and inference
- Representing and learning relations and properties under uncertainty
- An Advanced Deep Generative Framework for Temporal Link Prediction in Dynamic Networks
- Attacking Similarity-Based Link Prediction in Social Networks
- Constrained Relational Topic Models
- Deep Dynamic Mixed Membership Stochastic Blockmodel
- A Universal Method Based on Structure Subgraph Feature for Link Prediction over Dynamic Networks
Related papers
- Latent Variable Models
- Modeling Configural Patterns in Latent Variable Profiles: Association With an Endogenous Variable
- Nonlinear Effects in the Generalized Latent Variable Model
- PROPCNSREG: Stata module fitting a measurement model with causal indicators
- Information-theoretic latent distribution modeling: distinguishing discrete and continuous latent variable models.