Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations

• URL: http://arxiv.org/abs/2609.08813v2
• Date: Thu, 10 Sep 2026 18:08:46 GMT
• Title: Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relations
• Abstract summary: We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving proximity.<n>We propose a dynamic latent space model for inhomogeneous Poisson processes, where event intensities depend on node-specific activity parameters and time-varying latent distances modeled via flexible B-splines.
• Score: 1.988145627448243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study continuous-time relational event data, where time-stamped dyadic interactions reflect both individual node propensities and evolving relational proximity. We propose a dynamic latent space model for inhomogeneous Poisson processes, where event intensities depend on node-specific activity parameters and time-varying latent distances modeled via flexible B-splines. We prove model identifiability by decoupling baseline activity from latent position, ensuring high interaction volumes do not warp the spatial map. For scalability, we develop a minibatch stochastic gradient algorithm with stable initialization and geometric anchoring, alongside an effective-degrees-of-freedom BIC for tuning model complexity. Simulations confirm accurate parameter recovery and out-of-sample prediction. Applied to cooperative diplomatic events among 60 major economies (1995--2022), the model uncovers shifting patterns of international cooperation and isolates mobile geopolitical actors from stationary institutional anchors.

Related papers

• DyG$^2$T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer [86.4618122245946]
  DyG$2$T is a dynamics modeling framework that infers object motion trajectories by spatially completing and temporally discriminating Key Point representations.<n>Experiments on both synthetic and real-world datasets demonstrate that DyG$2$T achieves accurate dynamics modeling and reasoning.
  arXiv  Detail & Related papers  (2026-08-19T03:44:28Z)
• RopeDreamer: A Kinematic Recurrent State Space Model for Dynamics of Flexible Deformable Linear Objects [12.045392054359551]
  We propose a latent dynamics framework that combines a Recurrent State Space Model with a Quaternionic Kinematic Chain representation.<n>We evaluate our approach on a large-scale simulated dataset of complex pick-and-place trajectories involving self-intersections.<n>Our results demonstrate that the proposed model achieves a 40.52% reduction in open-loop prediction error over 50-step horizons compared to the state-of-the-art baseline.
  arXiv  Detail & Related papers  (2026-04-30T17:47:44Z)
• GeodesicNVS: Probability Density Geodesic Flow Matching for Novel View Synthesis [54.39598154430305]
  We propose a Data-to-Data Flow Matching framework that learns deterministic transformations directly between paired views.<n>PDG-FM constrains flow trajectories using geodesic interpolants derived from probability density metrics of pretrained diffusion models.<n>These results highlight the advantages of incorporating data-dependent geometric regularization into deterministic flow matching for consistent novel view generation.
  arXiv  Detail & Related papers  (2026-03-01T09:30:11Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring [13.472378581383628]
  This paper proposes an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring.<n>Three layers of temporal dilated causal convolutional network are used to capture long-range temporal correlations.<n>The model integrates the gated-activated original, local synchronous joint spatial-temporal, and long-range temporal features to achieve comprehensive prediction.
  arXiv  Detail & Related papers  (2025-04-10T18:32:56Z)
• How to Bridge Spatial and Temporal Heterogeneity in Link Prediction? A Contrastive Method [11.719027225797037]
  We propose a novel textbfContrastive Learning-based textbfLink textbfPrediction model, textbfCLP.<n>Our mymodel consistently outperforms the state-of-the-art models, demonstrating an average improvement of 10.10%, 13.44% in terms of AUC and AP.
  arXiv  Detail & Related papers  (2024-11-01T14:20:53Z)
• FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction [30.346763969306398]
  Mobile devices and the Internet of Things (IoT) devices nowadays generate a large amount of heterogeneous spatial-temporal data. It remains a challenging problem to model the spatial-temporal dynamics under privacy concern. We propose a novel Federated Adaptive spatial-temporal Attention (FedASTA) framework to model the dynamic spatial-temporal relations.
  arXiv  Detail & Related papers  (2024-05-21T11:44:07Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• Structured Optimal Variational Inference for Dynamic Latent Space Models [16.531262817315696]
  We consider a latent space model for dynamic networks, where our objective is to estimate the pairwise inner products plus the intercept of the latent positions. To balance posterior inference and computational scalability, we consider a structured mean-field variational inference framework.
  arXiv  Detail & Related papers  (2022-09-29T22:10:42Z)
• Manifold Interpolating Optimal-Transport Flows for Trajectory Inference [64.94020639760026]
  We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) MIOFlow learns, continuous population dynamics from static snapshot samples taken at sporadic timepoints. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
  arXiv  Detail & Related papers  (2022-06-29T22:19:03Z)
• A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networks [3.883893461313154]
  We propose a novel generative model for continuous-time networks of relational events using a latent space representation for nodes. We show that our proposed LSH model can replicate many features observed in real temporal networks including reciprocity and transitivity.
  arXiv  Detail & Related papers  (2022-05-19T00:56:12Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.