Mark Newman

Last updated
Mark Newman
Born
Alma mater Merton College, Oxford
Scientific career
FieldsPhysics
Institutions University of Michigan
Santa Fe Institute
Doctoral advisor David Sherrington

Mark Newman FRS is a British physicist and Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan, as well as an external faculty member of the Santa Fe Institute. He is known for his fundamental contributions to the fields of complex systems and complex networks, for which he was awarded the Lagrange Prize in 2014 and the APS Kadanoff Prize in 2024.

Contents

Career

Mark Newman grew up in Bristol, England, where he attended Bristol Cathedral School, and earned both an undergraduate degree and PhD in physics from the University of Oxford, before moving to the United States to conduct research first at Cornell University and later at the Santa Fe Institute. [1] In 2002 Newman moved to the University of Michigan, where he is currently the Anatol Rapoport Distinguished University Professor of Physics and a professor in the university's Center for the Study of Complex Systems.

Research

Newman is known for his research on complex networks, and in particular for work on random graph theory, assortative mixing, community structure, percolation theory, collaboration patterns of scientists, and network epidemiology. [2] In early work in collaboration with Steven Strogatz and Duncan Watts, he developed the theory of the configuration model, one of the standard models of network science, and associated mathematical methods based on probability generating functions. Around the same time he also popularized the concept of community structure in networks and the community detection problem, and worked on mixing patterns and assortativity in networks, both in collaboration with Michelle Girvan. In network epidemiology he published both on formal results, particularly concerning the connection between the SIR model and percolation, as well as practical applications to infections such as SARS, pneumonia, and group B strep. In later work he has focused on spectral graph theory and random matrices, belief propagation methods, and network reconstruction, among other things.

Newman has also worked on a range of topics outside of network theory in the general area of statistical physics, particularly on spin models and on percolation, where he is the inventor (with Robert Ziff) of the Newman-Ziff algorithm for computer simulation of percolation systems. [3] Outside of physics he has published papers in mathematics, computer science, biology, ecology, epidemiology, paleontology, and sociology. He has worked particularly on so-called power-law distributions, which govern the statistics of a wide range of systems from human populations and earthquakes to spoken languages and solar flares. [4] With Aaron Clauset and Cosma Shalizi, Newman developed statistical methods for analyzing power-law distributions and applied them to a wide range of systems, in various cases either confirming or refuting previously claimed power-law behaviors. [5] In other work, he was also the inventor, with Michael Gastner, of a method for generating density-equalizing maps or cartograms. Their work gained attention following the 2004 US presidential election when it was used as the basis for a widely circulated set of maps of the election results. [6] [7]

Newman's work is unusually well cited. A 2019 Stanford University study by John Ioannidis and collaborators ranked Newman as having the third highest citation impact of any active scientist in the world in any field, and the 28th highest of all time, out of 6.8 million scientists worldwide. [8] In 2021 Newman was named a Clarivate Citation Laureate, a distinction that recognizes scientists who have had "research influence comparable to that of Nobel Prize recipients". In the ten years following its publication, Newman's 2003 paper "The structure and function of complex networks" [9] was the most highly cited paper in the entire field of mathematics. [10]

Awards and honors

Newman is a Fellow of the Royal Society, Fellow of the American Physical Society, Fellow of the American Association for the Advancement of Science, Fellow of the Network Science Society, a Simons Foundation Fellow, and a Guggenheim Fellow. He was the recipient of the 2014 Lagrange Prize from the ISI Foundation, the 2021 Euler Award of the Network Science Society, and the 2024 Leo P. Kadanoff Prize of the American Physical Society.

See also

Selected publications

Books

Articles

Related Research Articles

<span class="mw-page-title-main">Scale-free network</span> Network whose degree distribution follows a power law

A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as

<span class="mw-page-title-main">Percolation</span> Filtration of fluids through porous materials

In physics, chemistry, and materials science, percolation refers to the movement and filtering of fluids through porous materials. It is described by Darcy's law. Broader applications have since been developed that cover connectivity of many systems modeled as lattices or graphs, analogous to connectivity of lattice components in the filtration problem that modulates capacity for percolation.

<span class="mw-page-title-main">Network theory</span> Study of graphs as a representation of relations between discrete objects

In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the nodes or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components.

<span class="mw-page-title-main">Small-world network</span> Graph where most nodes are reachable in a small number of steps

A small-world network is a graph characterized by a high clustering coefficient and low distances. On an example of social network, high clustering implies the high probability that two friends of one person are friends themselves. The low distances, on the other hand, mean that there is a short chain of social connections between any two people. Specifically, a small-world network is defined to be a network where the typical distance L between two randomly chosen nodes grows proportionally to the logarithm of the number of nodes N in the network, that is:

<span class="mw-page-title-main">Albert-László Barabási</span> Hungarian-American physicist (born 1967)

Albert-László Barabási is a Romanian-born Hungarian-American physicist, best known for his discoveries in network science and network medicine.

<span class="mw-page-title-main">Complex network</span> Network with non-trivial topological features

In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in networks representing real systems. The study of complex networks is a young and active area of scientific research inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, climate networks and social networks.

<span class="mw-page-title-main">Degree distribution</span>

In the study of graphs and networks, the degree of a node in a network is the number of connections it has to other nodes and the degree distribution is the probability distribution of these degrees over the whole network.

In the study of complex networks, assortative mixing, or assortativity, is a bias in favor of connections between network nodes with similar characteristics. In the specific case of social networks, assortative mixing is also known as homophily. The rarer disassortative mixing is a bias in favor of connections between dissimilar nodes.

<span class="mw-page-title-main">Preferential attachment</span> Stochastic process formalizing cumulative advantage

A preferential attachment process is any of a class of processes in which some quantity, typically some form of wealth or credit, is distributed among a number of individuals or objects according to how much they already have, so that those who are already wealthy receive more than those who are not. "Preferential attachment" is only the most recent of many names that have been given to such processes. They are also referred to under the names Yule process, cumulative advantage, the rich get richer, and the Matthew effect. They are also related to Gibrat's law. The principal reason for scientific interest in preferential attachment is that it can, under suitable circumstances, generate power law distributions. If preferential attachment is non-linear, measured distributions may deviate from a power law. These mechanisms may generate distributions which are approximately power law over transient periods.

<span class="mw-page-title-main">Community structure</span> Concept in graph theory

In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into sets of nodes such that each set of nodes is densely connected internally. In the particular case of non-overlapping community finding, this implies that the network divides naturally into groups of nodes with dense connections internally and sparser connections between groups. But overlapping communities are also allowed. The more general definition is based on the principle that pairs of nodes are more likely to be connected if they are both members of the same community(ies), and less likely to be connected if they do not share communities. A related but different problem is community search, where the goal is to find a community that a certain vertex belongs to.

In applied probability theory, the Simon model is a class of stochastic models that results in a power-law distribution function. It was proposed by Herbert A. Simon to account for the wide range of empirical distributions following a power-law. It models the dynamics of a system of elements with associated counters. In this model the dynamics of the system is based on constant growth via addition of new elements as well as incrementing the counters at a rate proportional to their current values.

Mixing patterns refer to systematic tendencies of one type of nodes in a network to connect to another type. For instance, nodes might tend to link to others that are very similar or very different. This feature is common in many social networks, although it also appears sometimes in non-social networks. Mixing patterns are closely related to assortativity; however, for the purposes of this article, the term is used to refer to assortative or disassortative mixing based on real-world factors, either topological or sociological.

<span class="mw-page-title-main">Percolation threshold</span> Threshold of percolation theory models

The percolation threshold is a mathematical concept in percolation theory that describes the formation of long-range connectivity in random systems. Below the threshold a giant connected component does not exist; while above it, there exists a giant component of the order of system size. In engineering and coffee making, percolation represents the flow of fluids through porous media, but in the mathematics and physics worlds it generally refers to simplified lattice models of random systems or networks (graphs), and the nature of the connectivity in them. The percolation threshold is the critical value of the occupation probability p, or more generally a critical surface for a group of parameters p1, p2, ..., such that infinite connectivity (percolation) first occurs.

Social network analysis (SNA) software is software which facilitates quantitative or qualitative analysis of social networks, by describing features of a network either through numerical or visual representation.

In the context of the physical and mathematical theory of percolation, a percolation transition is characterized by a set of universal critical exponents, which describe the fractal properties of the percolating medium at large scales and sufficiently close to the transition. The exponents are universal in the sense that they only depend on the type of percolation model and on the space dimension. They are expected to not depend on microscopic details such as the lattice structure, or whether site or bond percolation is considered. This article deals with the critical exponents of random percolation.

The webgraph describes the directed links between pages of the World Wide Web. A graph, in general, consists of several vertices, some pairs connected by edges. In a directed graph, edges are directed lines or arcs. The webgraph is a directed graph, whose vertices correspond to the pages of the WWW, and a directed edge connects page X to page Y if there exists a hyperlink on page X, referring to page Y.

Cristopher David Moore, known as Cris Moore, is an American computer scientist, mathematician, and physicist. He is resident faculty at the Santa Fe Institute, and was formerly a full professor at the University of New Mexico. He is an elected Fellow of the American Physical Society, the American Mathematical Society, and the American Association for the Advancement of Science.

<span class="mw-page-title-main">Scientific collaboration network</span>

Scientific collaboration network is a social network where nodes are scientists and links are co-authorships as the latter is one of the most well documented forms of scientific collaboration. It is an undirected, scale-free network where the degree distribution follows a power law with an exponential cutoff – most authors are sparsely connected while a few authors are intensively connected. The network has an assortative nature – hubs tend to link to other hubs and low-degree nodes tend to link to low-degree nodes. Assortativity is not structural, meaning that it is not a consequence of the degree distribution, but it is generated by some process that governs the network’s evolution.

Robustness, the ability to withstand failures and perturbations, is a critical attribute of many complex systems including complex networks.

Aaron Clauset is an American computer scientist who works in the areas of Network Science, Machine Learning, and Complex Systems. He is currently a professor of computer science at the University of Colorado Boulder and is external faculty at the Santa Fe Institute.

References

  1. Curriculum vitae, retrieved 2022-12-26.
  2. Mark Newman's home page
  3. Newman, M. E. J.; Ziff, R. M. (6 Nov 2000). "Efficient Monte Carlo algorithm and high-precision results for percolation". Physical Review Letters. 85 (19): 4014–4107. arXiv: cond-mat/0005264 . Bibcode:2000PhRvL..85.4104N. doi:10.1103/PhysRevLett.85.4104. PMID   11056635.
  4. Newman, M.E.J. (29 May 2006). "Power laws, Pareto distributions and Zipf's law". Contemporary Physics. 46: 323–351. arXiv: cond-mat/0412004 . doi:10.1016/j.cities.2012.03.001. S2CID   2871747.
  5. Clauset, Aaron; Shazili, Cosma Rohila; Newman, M. E. J. (2 Feb 2009). "Power-law distributions in empirical data". SIAM Review. 51 (4): 661–703. arXiv: 0706.1062 . Bibcode:2009SIAMR..51..661C. doi:10.1137/070710111. S2CID   9155618.
  6. Ehrenberg, Rachel (7 November 2012). "Red state, blue state". Science News. The Society for Science and the Public. Retrieved 8 April 2015.
  7. "Fifty shades of purple". Physics World. Institute of Physics. 12 November 2012. Retrieved 8 April 2015.
  8. Ioannidis, John P. A.; Baas, Jeroen; Klavans, Richard; Boyack, Kevin W. (12 Aug 2019). "A standardized citation metrics author database annotated for scientific field". PLOS Biology. 17 (8): e3000384. doi: 10.1371/journal.pbio.3000384 . PMC   6699798 . PMID   31404057.
  9. Newman, Mark E. J. (June 2003). "The structure and function of complex networks". SIAM Review. 45 (2): 167–256. arXiv: cond-mat/0303516 . Bibcode:2003SIAMR..45..167N. doi:10.1137/S003614450342480. S2CID   221278130.
  10. "Top institutions in Mathematics". Times Higher Education. 2 June 2011. Retrieved 8 April 2015.