Mark Newman | |
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Born | |
Alma mater | Merton College, Oxford |
Scientific career | |
Fields | Physics |
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.
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.
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]
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.
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
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.
In mathematics, computer science and network science, network theory is a part of graph theory. It defines networks as graphs where the vertices or edges possess attributes. Network theory analyses these networks over the symmetric relations or asymmetric relations between their (discrete) components.
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.
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 physics, the Tsallis entropy is a generalization of the standard Boltzmann–Gibbs entropy. It is proportional to the expectation of the q-logarithm of a distribution.
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.
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.
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.
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.
Modularity is a measure of the structure of networks or graphs which measures the strength of division of a network into modules. Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. Modularity is often used in optimization methods for detecting community structure in networks. Biological networks, including animal brains, exhibit a high degree of modularity. However, modularity maximization is not statistically consistent, and finds communities in its own null model, i.e. fully random graphs, and therefore it cannot be used to find statistically significant community structures in empirical networks. Furthermore, it has been shown that modularity suffers a resolution limit and, therefore, it is unable to detect small communities.
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.
Maya Paczuski is the head and founder of the Complexity Science Group at the University of Calgary. She is a well-cited physicist whose work spans self-organized criticality, avalanche dynamics, earthquake, and complex networks. She was born in Israel in 1963, but grew up in the United States. Maya Paczuski received a B.S. and M.S. in Electrical Engineering and Computer Science from M.I.T. in 1986 and then went on to study with Mehran Kardar, earning her Ph.D in Condensed matter physics from the same institute.
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.
Price's model is a mathematical model for the growth of citation networks. It was the first model which generalized the Simon model to be used for networks, especially for growing networks. Price's model belongs to the broader class of network growing models whose primary target is to explain the origination of networks with strongly skewed degree distributions. The model picked up the ideas of the Simon model reflecting the concept of rich get richer, also known as the Matthew effect. Price took the example of a network of citations between scientific papers and expressed its properties. His idea was that the way an old vertex gets new edges should be proportional to the number of existing edges the vertex already has. This was referred to as cumulative advantage, now also known as preferential attachment. Price's work is also significant in providing the first known example of a scale-free network. His ideas were used to describe many real-world networks such as the Web.
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.