mlsanityData to 7 Oct 2026

Intensity Profile Projection: A Framework for Continuous-Time Representation Learning for Dynamic Networks

arXiv 2306.06155 ↗First posted 9 Jun 2023cs.LG stat.ME stat.MLcited by 5

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Abstract (arXiv)

We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples (i,j,t), each representing a time-stamped (t) interaction between two entities (i,j), our procedure returns a continuous-time trajectory for each node, representing its behaviour over time. The framework consists of three stages: estimating pairwise intensity functions, e.g. via kernel smoothing; learning a projection which minimises a notion of intensity reconstruction error; and constructing evolving node representations via the learned projection. The trajectories satisfy two properties, known as structural and temporal coherence, which we see as fundamental for reliable inference. Moreoever, we develop estimation theory providing tight control on the error of any estimated trajectory, indicating that the representations could even be used in quite noise-sensitive follow-on analyses. The theory also elucidates the role of smoothing as a bias-variance trade-off, and shows how we can reduce the level of smoothing as the signal-to-noise ratio increases on account of the algorithm `borrowing strength' across the network.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 31 of its 74 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

31 of 74 references.

References · 31

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19 more
  1. 2020The multilayer random dot product graphcited by 18
  2. 2016Limit theorems for eigenvectors of the normalized Laplacian for random graphscited by 17
  3. 2020Manifold structure in graph embeddingscited by 17
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  8. 2022A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networkscited by 10
  9. 2022Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representationscited by 10
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Cited by year (2026 to 7 Oct)

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

5 papers.

  1. 2024A Primer on Temporal Graph Learningcited by 4
  2. 2025Multiresolution Analysis and Statistical Thresholding on Dynamic Networkscited by 1
  3. 2024Generalized Grade-of-Membership Estimation for High-dimensional Locally Dependent Datacited by 0
  4. 2025Unsupervised Attributed Dynamic Network Embedding with Stability Guaranteescited by 0
  5. 2026Non-Negative Matrix Factorization for Event Datacited by 0