Hernán A. Makse

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Reference excerpt

Hernán A. Makse is an Argentine-American physicist. He is a Distinguished Professor of Physics at the City College of New York (CUNY) and he is affiliated with the Benjamin Levich Institute, where he leads the Complex Networks and Data Science Lab. He is known for contributions to granular matter and packings, complex systems, complex networks, biological networks, and artificial intelligence, spanning from condensed matter physics to the study of cities, social media, influential spreaders, brain networks, and large language models.

Education and career Makse earned a Ph.D. in physics from Boston University under the supervision of Prof. H. E. Stanley. He held a postdoctoral positions at the Cavendish Laboratory at the University of Cambridge and the Collège de France in Paris (with Pierre-Gilles de Gennes). He was also a Postdoctoral Fellow at Schlumberger in Ridgefield, Connecticut, in the laboratory of David Linton Johnson, before joining the City College of New York (CUNY), where he is a Distinguished Professor of Physics. He is also a professor in the CUNY Neuroscience Collaborative (Biology/Psychology PhD programs) at the CUNY Graduate Center, and serves as an Affiliate Attending Imaging Scientist at Memorial Sloan Kettering Cancer Center(Radiology/Neuroradiology) applying machine learning to cancer screening and brain imaging.

Research Early in his career, Makse and collaborators developed statistical-physics approaches to granular materials, stratification in granular mixtures, and jamming of random packings. In network science, he was among the authors who reported self-similarity and fractality in real-world networks and developed the Collective Influence algorithm and k-core decomposition to identify key spreaders in networks. Building on these tools, Makse and collaborators carried out empirical studies of influencers in social media and networks. In biological networks, Makse and collaborators developed the symmetry-fibration framework (see Fibration) to describe structural building blocks and cluster synchronization across systems ranging from gene regulation to neural connectomes. These methods build on the categorical notion of fibration introduced by Grothendieck and later adapted to fibrations of graphs by Paolo Boldi and Sebastiano Vigna. Related work connected symmetry fibrations to machine learning and geometric deep learning, including local-symmetry perspectives for graph neural networks and deep neural networks.

Awards and honors 2003 – Received an NSF CAREER Award for research on the statistical mechanics of particulate systems far from equilibrium. 2005 – Received the New York City Mayor's Young Investigator Award for Excellence in Science and Technology (New York Academy of Sciences). 2007 – Received a César Milstein SubProgram Subsidio (Secretaría de Ciencia y Técnica, Programa RAÍCES, Argentina). 2012 – Elected a Fellow of the American Physical Society. 2023 – Elected a corresponding member of the Brazilian Academy of Sciences.

Books Makse, Hernán A.; Boldi, Paolo; Sorrentino, Francesco; Stewart, Ian (2026). Symmetries of Living and Artificial Intelligence Systems: Fibrations and Synchronization in Networks. Cambridge University Press. arXiv:2502.18713. Makse, Hernán A.; Zava, Marta (2025). The Science of Influencers and Superspreaders: Using Networks and Artificial Intelligence to Understand Fake News, Pandemics, Markets, and the Brain. Springer Cham. doi:10.1007/978-3-031-78058-5. ISBN 978-3-031-78058-5. Retrieved 2026-02-22.

Selected publications Makse, Hernán A.; Havlin, Shlomo; Stanley, H. Eugene (1995). "Modelling urban growth patterns". Nature. 377 (6550): 608–612. Bibcode:1995Natur.377..608M. doi:10.1038/377608a0. Makse, Hernán A.; Havlin, Shlomo; King, Peter R.; Stanley, H. Eugene (1997). "Spontaneous stratification in granular mixtures". Nature. 386 (6623): 379–382. arXiv:cond-mat/9809432. Bibcode:1997Natur.386..379M. doi:10.1038/386379a0. Makse, Hernán A.; Kurchan, Jorge (2002). "Testing the thermodynamic approach to granular matter with a numerical model of a decisive experiment". Nature. 415 (6872): 614–617. Bibcode:2002Natur.415..614M. doi:10.1038/415614a. PMID 11832938. Song, Chaoming; Havlin, Shlomo; Makse, Hernán A. (2005). "Self-similarity of complex networks". Nature. 433 (7024): 392–395. arXiv:cond-mat/0503078. Bibcode:2005Natur.433..392S. doi:10.1038/nature03248. PMID 15674285. Song, Chaoming; Wang, Ping; Makse, Hernán A. (2008). "A phase diagram for jammed matter". Nature. 453 (7195): 629–632. arXiv:0808.2196. Bibcode:2008Natur.453..629S. doi:10.1038/nature06981. PMID 18509438. Kitsak, Maksim; Gallos, Lazaros K.; Havlin, Shlomo; Liljeros, Fredrik; Muchnik, Lev; Stanley, H. Eugene; Makse, Hernán A. (2010). "Identification of influential spreaders in complex networks". Nature Physics. 6 (11): 888–893. arXiv:1001.5285. Bibcode:2010NatPh...6..888K. doi:10.1038/nphys1746. Morone, Flaviano; Makse, Hernán A. (2015). "Influence maximization in complex networks through optimal percolation". Nature. 524 (7563): 65–68. arXiv:1506.08326. Bibcode:2015Natur.524...65M. doi:10.1038/nature14604. PMID 26131931. Morone, Flaviano; Makse, Hernán A. (2019). "Symmetry group factorization reveals the structure-function relation in the neural connectome of Caenorhabditis elegans". Nature Communications. 10 (1): 4961. arXiv:1908.10923. Bibcode:2019NatCo..10.4961M. doi:10.1038/s41467-019-12675-8. PMC 6823386. PMID 31672985.

References

External links Hernán A. Makse on Google Scholar

Illustrations

Worked examples

Example 1 — a first encounter with Hernán A. Makse

Start with the simplest possible case. Write down what Hernán A. Makse claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Hernán A. Makse before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Hernán A. Makse ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Hernán A. Makse

In research: Hernán A. Makse appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Hernán A. Makse in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Hernán A. Makse is common in secondary-school and first-year university syllabi. It links to neighbouring topics 1966 births, 20th-century American physicists, 21st-century American physicists, so understanding it makes those chapters shorter.
In everyday life: Look for Hernán A. Makse outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

How to study Hernán A. Makse in 20 minutes

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Frequently asked questions

What is Hernán A. Makse in simple terms?

Hernán A. Makse is an Argentine-American physicist.

Why does Hernán A. Makse matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Hernán A. Makse?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

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