mlsanityData to 7 Oct 2026

Continuous Latent Position Models for Instantaneous Interactions

arXiv 2103.17146 ↗First posted 31 Mar 2021stat.ME stat.MLcited by 8

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Abstract (arXiv)

We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities. This type of interaction data is especially common nowadays, and easily available. Examples of instantaneous interactions include email networks, phone call networks and some common types of technological and transportation networks. Our framework relies on a novel extension of the latent position network model: we assume that the entities are embedded in a latent Euclidean space, and that they move along individual trajectories which are continuous over time. These trajectories are used to characterize the timing and frequency of the pairwise interactions. We discuss an inferential framework where we estimate the individual trajectories from the observed interaction data, and propose applications on artificial and real data.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 23 of its 54 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

23 of 54 references.

References · 23

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  2. 2020Latent Space Models for Dynamic Networkscited by 29
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11 more
  1. 2020Latent Space Models for Dynamic Networks with Weighted Edgescited by 6
  2. 2020A Review of Latent Space Models for Social Networkscited by 5
  3. 2015Properties of Latent Variable Network Modelscited by 4
  4. 2016Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networkscited by 3
  5. 2018Computationally efficient inference for latent position network modelscited by 3
  6. 2013Temporal networks of face-to-face human interactionscited by 2
  7. 2017Process convolution approaches for modeling interacting trajectoriescited by 2
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  10. 2019Measuring systemic risk and contagion in the European financial networkcited by 1
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 2; 2026: 1.

8 papers.

  1. 2022A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networkscited by 10
  2. 2023Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networkscited by 5
  3. 2024Gaussian Embedding of Temporal Networkscited by 3
  4. 2022SimHawNet: A Modified Hawkes Process for Temporal Network Simulationcited by 1
  5. 2023Fast inference of latent space dynamics in huge relational event networkscited by 1
  6. 2025Multiresolution Analysis and Statistical Thresholding on Dynamic Networkscited by 1
  7. 2025Spectral clustering for dependent community Hawkes process models of temporal networkscited by 0
  8. 2026Dynamic Latent Space Modeling of Inhomogeneous Poisson Network Processes with Applications to International Relationscited by 0