Modeling heterogeneity in random graphs through latent space models: a selective review
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Abstract (arXiv)
We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and their extensions that have undergone major developments in the last five years.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2014.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.
7 papers.
- 2022A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representationscited by 9
- 2023Conformal link prediction for false discovery rate controlcited by 9
- 2021Error rate control for classification rules in multiclass mixture modelscited by 2
- 2021A Generative Node-attribute Network Model for Detecting Generalized Structurecited by 1
- 2025Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distancecited by 1
- 2025Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animalscited by 0
- 2026Low-Complexity and Consistent Graphon Estimation from Multiple Networkscited by 0
- 2026Low-Complexity and Consistent Graphon Estimation from Multiple Networks16 Mar 2026
- 2025Model-free algorithms for fast node clustering in SBM type graphs and application to social role inference in animals19 Sep 2025
- 2025Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance17 Feb 2025
- 2023Conformal link prediction for false discovery rate control26 Jun 2023
- 2022A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations12 Apr 2022
- 2021Error rate control for classification rules in multiclass mixture models29 Sep 2021
- 2021A Generative Node-attribute Network Model for Detecting Generalized Structure5 Jun 2021