Mark Newman

University of Michigan

CombinatoricsPhysicsMathematicsComputer scienceRandom graph

248Publications

89H-index

108kCitations

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Publications 237

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#1Mark NewmanH-Index: 89

#2George T. CantwellH-Index: 2

Last. Jean-Gabriel YoungH-Index: 5

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: The information theoretic measure known as mutual information is widely used as a way to quantify the similarity of two different labelings or divisions of the same set of objects, such as arises, for instance, in clustering and classification problems in machine learning or community detection problems in network science. Here we argue that the standard mutual information, as commonly defined, omits a crucial term which can become large under real-world conditions, producing results that can ...

#1Mark Newman (UM: University of Michigan)H-Index: 89

#2Carrie R. Ferrario (UM: University of Michigan)H-Index: 20

The incorporation of microeconomics concepts into studies using self-administration procedures has provided critical insights into the factors that influence consumption of a wide range of food and drug reinforcers. In particular, the fitting of demand curves to consumption data provides a powerful analytic tool for computing objective metrics of behavior that can be compared across a wide range of reward types in both human and animal experiments. The results of these analyses depend crucially ...

#1George T. CantwellH-Index: 2

#2Mark NewmanH-Index: 89

In this paper we offer a solution to a long-standing problem in the study of networks. Message passing is a fundamental technique for calculations on networks and graphs. The first versions of the method appeared in the 1930s and over the decades it has been applied to a wide range of foundational problems in mathematics, physics, computer science, statistics, and machine learning, including Bayesian inference, spin models, coloring, satisfiability, graph partitioning, network epidemiology, and ...

#1Mark Newman (UM: University of Michigan)H-Index: 89

#1Mark W. Newman (UM: University of Michigan)H-Index: 29

Last. Carrie R. Ferrario (UM: University of Michigan)H-Index: 20

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The incorporation of microeconomics concepts into studies using preclinincal self-administration procedures has provided critical insights into the factors that influence consumption of a wide range of food and drug reinforcers. In particular, the fitting of demand curves to consumption data provides a powerful analytic tool for computing objective metrics of behavior that can be compared across a wide range of reward types and experimental settings. The results of these analyses depend cruciall...

#1Jean-Gabriel Young (UM: University of Michigan)H-Index: 1

#2Fernanda S. Valdovinos (UM: University of Michigan)H-Index: 12

Last. Mark Newman (UM: University of Michigan)H-Index: 89

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Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but a...

#1Maria A. RioloH-Index: 8

#2Mark NewmanH-Index: 89

The most widely used techniques for community detection in networks, including methods based on modularity, statistical inference, and information theoretic arguments, all work by optimizing objective functions that measure the quality of network partitions. There is a good case to be made, however, that one should not look solely at the single optimal community structure under such an objective function, but rather at a selection of high-scoring structures. If one does this one typically finds ...

#1Mark NewmanH-Index: 89

#2George T. CantwellH-Index: 2

Last. Jean-Gabriel YoungH-Index: 1

view all 3 authors...

The information theoretic quantity known as mutual information finds wide use in classification and community detection analyses to compare two classifications of the same set of objects into groups. In the context of classification algorithms, for instance, it is often used to compare discovered classes to known ground truth and hence to quantify algorithm performance. Here we argue that the standard mutual information, as commonly defined, omits a crucial term which can become large under real...

#1Mark Newman (UM: University of Michigan)H-Index: 89

Jan 1, 2019 in DIS (Designing Interactive Systems)

#1Xinghui Yan (UM: University of Michigan)

#2Katy Madier (UM: University of Michigan)H-Index: 1

Last. Mark W. Newman (UM: University of Michigan)H-Index: 29

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#1Mark NewmanH-Index: 89

#2Xiao ZhangH-Index: 5

Last. Raj Rao NadakuditiH-Index: 14

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We derive a message passing method for computing the spectra of locally tree-like networks and an approximation to it that allows us to compute closed-form expressions or fast numerical approximates for the spectral density of random graphs with arbitrary node degrees -- the so-called configuration model. We find the latter approximation to work well for all but the sparsest of networks. We also derive bounds on the position of the band edges of the spectrum, which are important for identifying ...

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