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Our advice: Be fractal and be robust

Methods developed in the framework of statistical physics have provided a completely new understanding of complex networks. Similar structural properties have been uncovered in networks found in fields as diverse as biology, technology and sociology. In particular, certain real-world networks display fractality, resembling the self-similarity in Mandelbrot’s famous geometric sets. But can these organizational principles point us to potential common mechanisms underlying the growth of complex networks? Chaoming Song [1] and colleagues show that, indeed, certain classes of networks do evolve according to shared principles, and that these can be formulated in terms of renormalization theory. Furthermore, they find that fractality provides protection against intentional attacks. For biological networks, this might suggest an evolutionary drive towards fractality. References:

[1] C. Song, S. Havlin, and H. A. Makse, Origins of Fractality in the Growth of Complex Networks, Nature Physics 2, 275-281 (2006). ps version , pdf version . Pdf version including supplementary materials.

From the April 2006 issue of Nature Physics.

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October 21st, 2020

Our quest to break down complex systems is the catalyst behind recent advances at Complex Networks and Data Science Lab, including a COVID-19 digital contact tracing app in Latin America that uses network theory

See press release at: http://www1.cuny.edu/mu/forum/2020/10/08/1m-nih-grant-for-ccny-based-hernan-makse-led-complex-systems-research/https://www.ccny.cuny.edu/news/1m-nih-grant-ccny-based-hernan-makse-led-complex-systems-researchOur quest to break down complex systems is the catalyst behind recent advances at Complex Networks and Data […]

August 29th, 2019

Tequila celebration of Qiongge Li PhD defense. August 2019.

April 12th, 2018

CCNY physicist tracks influence of fake news on Presidential election

    Current events have brought the spread of false information via social media to the forefront of national consciousness. […]

February 5th, 2017

Science: Predictions: The Pulse of the People

Volume 355 of Science (February 03, 2017) focuses on the use of scientific methods to predict population-level trends. The work […]

November 27th, 2016

Predicting election trends with Twitter: Hillary Clinton versus Donald Trump.

The recently-concluded United States Presidential election was the end to one of the most divisive and vitriolic campaign seasons in […]

August 6th, 2015

Nature: Optimal Percolation: Destruction perfected

NATURE | NEWS & VIEWS Nature 524, 38–39 (06 August 2015) In complex networks, some nodes are more important than others. […]

October 6th, 2014

Nature Physics: Brain Network of Networks: Why natural networks are more stable than man-made networks

Connecting complex networks is known to exacerbate perturbations and lead to cascading failures, but natural networks of networks like the […]

May 6th, 2014

MIT Tech Review: The Emerging Science of Superspreaders (And How to Tell If You’re One Of Them)

From MIT Technology Review. Nobody has figured out how to spot the most influential spreaders of information in a real-world […]

February 6th, 2014

Scientific Reports: Large cities are less green but help to reduce suicidal rates

Work done in collaboration with Jose S. Andrade from Universidade Federal de Ceara, Brazil. Large cities are more productive than […]

February 6th, 2014

Soft Matter: Fundamental challenges in packing problems: from spherical to non-spherical particles

by Adrian Baule and Hernan Makse. Random packings of objects of a particular shape are ubiquitous in science and engineering. […]