mlsanityData to 7 Oct 2026

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

arXiv 1606.01855 ↗First posted 6 Jun 2016stat.ML cs.AI cs.LGcited by 25

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Abstract (arXiv)

We introduce Bayesian Poisson Tucker decomposition (BPTD) for modeling country--country interaction event data. These data consist of interaction events of the form "country i took action a toward country j at time t." BPTD discovers overlapping country--community memberships, including the number of latent communities. In addition, it discovers directed community--community interaction networks that are specific to "topics" of action types and temporal "regimes." We show that BPTD yields an efficient MCMC inference algorithm and achieves better predictive performance than related models. We also demonstrate that it discovers interpretable latent structure that agrees with our knowledge of international relations.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jun 2016.

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Citing papers per year: 2021: 2; 2022: 6; 2023: 1; 2024: 8; 2025: 3; 2026: 5.

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  1. 2022Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimationcited by 8
  2. 2022Nonparametric Factor Trajectory Learning for Dynamic Tensor Decompositioncited by 7
  3. 2021Nonparametric Sparse Tensor Factorization with Hierarchical Gamma Processescited by 6
  4. 2022An Ordinal Latent Variable Model of Conflict Intensitycited by 6
  5. 2022Nonparametric Embeddings of Sparse High-Order Interaction Eventscited by 5
  6. 2023Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processescited by 5
  7. 2024Efficient Nonparametric Tensor Decomposition for Binary and Count Datacited by 3
  8. 2025Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networkscited by 3
  9. 2022The Ordered Matrix Dirichlet for State-Space Modelscited by 2
  10. 2024Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysiscited by 2
  11. 2025Guaranteed Multidimensional Time Series Prediction via Deterministic Tensor Completion Theorycited by 2
  12. 2024Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Seriescited by 1
13 more
  1. 2024Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Datacited by 1
  2. 2021Applying Differential Privacy to Tensor Completioncited by 0
  3. 2022Robust Manifold Nonnegative Tucker Factorization for Tensor Data Representationcited by 0
  4. 2024Wasserstein Nonnegative Tensor Factorization with Manifold Regularizationcited by 0
  5. 2024A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamicscited by 0
  6. 2024Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMCcited by 0
  7. 2024The ALℓ0CORE Tensor Decomposition for Sparse Count Datacited by 0
  8. 2025Exact Recovery of Non-Random Missing Multidimensional Time Series via Temporal Isometric Delay-Embedding Transformcited by 0
  9. 2026Kronecker-Structured Nonparametric Spatiotemporal Point Processescited by 0
  10. 2026Bayesian Tensor Decomposition with Diffusion Model Priorcited by 0
  11. 2026Bayesian Poisson-Randomized Gamma Tensor Factorization with Application to International Trade Flowscited by 0
  12. 2026Poisson-Gamma Dynamical Systems with Time-varying Transition Dynamicscited by 0
  13. 2026Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relationscited by 0