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Mark Newman (other)
Mark Newman 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. 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.Curriculum vitae retrieved 2022-12-26. In 2002 Newman moved to the [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bristol
Bristol () is a city, ceremonial county and unitary authority in England. Situated on the River Avon, it is bordered by the ceremonial counties of Gloucestershire to the north and Somerset to the south. Bristol is the most populous city in South West England. The wider Bristol Built-up Area is the eleventh most populous urban area in the United Kingdom. Iron Age hillforts and Roman villas were built near the confluence of the rivers Frome and Avon. Around the beginning of the 11th century, the settlement was known as (Old English: 'the place at the bridge'). Bristol received a royal charter in 1155 and was historically divided between Gloucestershire and Somerset until 1373 when it became a county corporate. From the 13th to the 18th century, Bristol was among the top three English cities, after London, in tax receipts. A major port, Bristol was a starting place for early voyages of exploration to the New World. On a ship out of Bristol in 1497, John Cabot, a Venetia ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Community Structure
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 (potentially overlapping) 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. Properties In the study of networks, such as computer and information networks, social networks and biological networks, a number of different charac ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cartogram
A cartogram (also called a value-area map or an anamorphic map, the latter common among German-speakers) is a thematic map of a set of features (countries, provinces, etc.), in which their geographic size is altered to be directly proportional to a selected ratio-level variable, such as travel time, population, or GNP. Geographic space itself is thus warped, sometimes extremely, in order to visualize the distribution of the variable. It is one of the most abstract types of map; in fact, some forms may more properly be called diagrams. They are primarily used to display emphasis and for analysis as nomographs. Cartograms leverage the fact that size is the most intuitive visual variable for representing a total amount.Jacque Bertin, ''Sémiologie Graphique. Les diagrammes, les réseaux, les cartes''. With Marc Barbut t al. Paris : Gauthier-Villars. ''Semiology of Graphics'', English Edition, Translation by William J. Berg, University of Wisconsin Press, 1983.) In this, it is a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cosma Shalizi
Cosma Rohilla Shalizi (born February 28, 1974) is an associate professor in the Department of Statistics at Carnegie Mellon University in Pittsburgh. Life Cosma Rohilla Shalizi is of Tamil, Afghan and Italian heritage and was born in Boston, where he lived for the first two years of his life. He grew up in Bethesda, Maryland. In 1990 he was accepted as a Chancellor's Scholar at the University of California, Berkeley, and completed a bachelor's degree in Physics. Subsequently, he attended the University of Wisconsin–Madison where he received a doctorate in physics in May 2001. From 1998 to 2002, he worked at the Santa Fe Institute, in the Evolving Cellular Automata Project and the Computation, Dynamics and Inference group. From 2002 to 2005, he worked at the Center for the Study of Complex Systems at the University of Michigan in Ann Arbor. In August 2006, he became an assistant professor in the Department of Statistics at Carnegie Mellon University in Pittsburgh. Shalizi i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aaron Clauset
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. Education Clauset completed his undergraduate studies in physics and computer science at Haverford College in 2001.Curriculum vitae retrieved 2016-06-30. He earned his Ph.D. in Computer Science in 2006 from the University of New Mexico under the supervision of . He was then an Omidyar Fellow at the [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Power Law
In statistics, a power law is a Function (mathematics), functional relationship between two quantities, where a Relative change and difference, relative change in one quantity results in a proportional relative change in the other quantity, independent of the initial size of those quantities: one quantity varies as a Exponentiation, power of another. For instance, considering the area of a square in terms of the length of its side, if the length is doubled, the area is multiplied by a factor of four. Empirical examples The distributions of a wide variety of physical, biological, and man-made phenomena approximately follow a power law over a wide range of magnitudes: these include the sizes of craters on the moon and of solar flares, the foraging pattern of various species, the sizes of activity patterns of neuronal populations, the frequencies of words in most languages, frequencies of family names, the species richness in clades of organisms, the sizes of power outages, volcanic ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Physics
Statistical physics is a branch of physics that evolved from a foundation of statistical mechanics, which uses methods of probability theory and statistics, and particularly the Mathematics, mathematical tools for dealing with large populations and approximations, in solving physical problems. It can describe a wide variety of fields with an inherently stochastic nature. Its applications include many problems in the fields of physics, biology, chemistry, and neuroscience. Its main purpose is to clarify the properties of matter in aggregate, in terms of physical laws governing atomic motion. Statistical mechanics develop the Phenomenology (particle physics), phenomenological results of thermodynamics from a probabilistic examination of the underlying microscopic systems. Historically, one of the first topics in physics where statistical methods were applied was the field of classical mechanics, which is concerned with the motion of particles or objects when subjected to a force. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
SIR Model
Compartmental models are a very general modelling technique. They are often applied to the mathematical modelling of infectious diseases. The population is assigned to compartments with labels – for example, S, I, or R, (Susceptible, Infectious, or Recovered). People may progress between compartments. The order of the labels usually shows the flow patterns between the compartments; for example SEIS means susceptible, exposed, infectious, then susceptible again. The origin of such models is the early 20th century, with important works being that of Ross in 1916, Ross and Hudson in 1917, Kermack and McKendrick in 1927 and Kendall in 1956. The Reed-Frost model was also a significant and widely-overlooked ancestor of modern epidemiological modelling approaches. The models are most often run with ordinary differential equations (which are deterministic), but can also be used with a stochastic (random) framework, which is more realistic but much more complicated to analyze. Mod ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Michelle Girvan
Michelle Girvan (born 1977) is an American physicist and network scientist whose research combines methods from dynamical systems, graph theory, and statistical mechanics and applies them to problems including epidemiology, gene regulation, and the study of Information cascades. She is one of the namesakes of the Girvan–Newman algorithm, used to detect community structure in complex systems. Girvan is a professor of physics at the University of Maryland, College Park. Education and career Girvan graduated from the Massachusetts Institute of Technology in 1999, with a double major in mathematics and physics and a minor in political science. She completed a Ph.D. in physics at Cornell University in 2004. Her dissertation, ''The Structure and Dynamics of Complex Networks'', was supervised by Steven Strogatz. After postdoctoral research at the Santa Fe Institute, she joined the University of Maryland faculty in 2007. Recognition In 2017 Girvan was named a Fellow of the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Probability-generating Function
In probability theory, the probability generating function of a discrete random variable is a power series representation (the generating function) of the probability mass function of the random variable. Probability generating functions are often employed for their succinct description of the sequence of probabilities Pr(''X'' = ''i'') in the probability mass function for a random variable ''X'', and to make available the well-developed theory of power series with non-negative coefficients. Definition Univariate case If ''X'' is a discrete random variable taking values in the non-negative integers , then the ''probability generating function'' of ''X'' is defined as http://www.am.qub.ac.uk/users/g.gribakin/sor/Chap3.pdf :G(z) = \operatorname (z^X) = \sum_^p(x)z^x, where ''p'' is the probability mass function of ''X''. Note that the subscripted notations ''G''''X'' and ''pX'' are often used to emphasize that these pertain to a particular random variable ''X'', and to its ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Configuration Model
In network science, the configuration model is a method for generating random networks from a given degree sequence. It is widely used as a reference model for real-life social networks, because it allows the modeler to incorporate arbitrary degree distributions. Rationale for the model In the configuration model, the degree of each vertex is pre-defined, rather than having a probability distribution from which the given degree is chosen. As opposed to the Erdős–Rényi model, the degree sequence of the configuration model is not restricted to have a Poisson distribution, the model allows the user to give the network any desired degree distribution. Algorithm The following algorithm describes the generation of the model: # Take a degree sequence, i. e. assign a degree k_ito each vertex. The degrees of the vertices are represented as half-links or stubs. The sum of stubs must be even in order to be able to construct a graph (\sum k_i = 2m ). The degree sequence can ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Duncan Watts
Duncan James Watts (born February 20, 1971) is a sociologist and a professor at the University of Pennsylvania. He was formerly a principal researcher at Microsoft Research in New York City, and is known for his work on small-world networks. Education Watts received a Bachelor of Science degree in physics from the University of New South Wales and a PhD in Theoretical and Applied Mechanics from Cornell University, where his advisor was Steven Strogatz. Career Watts joined the faculty of the University of Pennsylvania in July 2019 as a PIK Professor. He has joint appointments in Engineering, Communications and Business. Watts was past external faculty member of the Santa Fe Institute and a former professor of sociology at Columbia University, where he headed the Collective Dynamics Group. He is also author of two books. His first, '' Six Degrees: The Science of a Connected Age'' is based on the six degrees research in his 1998 paper with Steven Strogatz, in which the two prese ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
