Aurélien Decelle: h-index, Total Citations, and Citation Map
Aurélien Decelle's h-index is 22 (39 i10-index, 2,888+ total citations across 5+ publications) according to Google Scholar as of June 2026. Aurélien Decelle is affiliated with Research, Universidad Politécnica de Madrid.
Aurélien Decelle is a researcher affiliated with Research, Universidad Politécnica de Madrid, specializing in statistical physics, machine learning, Bayesian inference. Their work has been cited 2,888 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Aurélien Decelle's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 4 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 22
- i10-Index
- 39
- Total Citations
- 2,888
- Citing Countries
- 21
As of June 2026.
Aurélien Decelle has an h-index of 22 and 2,888 total citations across 5 publications, with research cited by institutions in 21 countries.
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Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications
20111,030
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Top Citing Institutions
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Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher established a theoretical framework for detecting modules in sparse networks, characterizing the inference limits and phase transitions that define detectability in complex systems.
The researcher established a theoretical framework linking Archimedean lattices to the bound states of wave-interacting particles, a contribution recognized by 90 citations.
The researcher established a foundational asymptotic framework for analyzing modular networks via the stochastic block model, enabling rigorous algorithmic applications in community detection.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Aurélien Decelle's research
Aurélien Decelle is a researcher in statistical physics, machine learning and Bayesian inference at Research, Universidad Politécnica de Madrid. Their work has been cited 2,888 times across 5 publications (h-index 22), according to Google Scholar.
Their most-cited work, “Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications” (2011), has accumulated 1,030 citations. Other influential works include “Inference and phase transitions in the detection of modules in sparse networks” (2011) with 465 citations and “Creating artificial human genomes using generative neural networks” (2021) with 181 citations.
Citations of Aurélien Decelle's research come primarily from United States, France and Spain, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
