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New model analyzes international relations using dynamic latent spaces

By PulseAugur Editorial · [1 sources] ·

A new paper introduces a dynamic latent space model for analyzing inhomogeneous Poisson network processes, particularly useful for understanding international relations. The model uses flexible B-splines to capture evolving relational proximity and node-specific activity, with a novel BIC for complexity tuning and a minibatch stochastic gradient algorithm for scalability. When applied to cooperative diplomatic events among major economies from 1995 to 2022, the model identified shifting cooperation patterns and distinguished between mobile geopolitical actors and stationary institutional anchors. AI

IMPACT Introduces a novel statistical modeling technique applicable to complex network data, potentially enhancing analysis in fields like international relations.

RANK_REASON The cluster contains a pre-print academic paper detailing a new statistical model. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New model analyzes international relations using dynamic latent spaces

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The cluster contains a pre-print academic paper detailing a new statistical model. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Jie Jian, Jiguo Cao, Owen G. Ward ·

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

    arXiv:2609.08813v2 Announce Type: replace-cross 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 …

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