D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 39 Citations 7,404 246 World Ranking 4634 National Ranking 122

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Quantum mechanics
  • Artificial intelligence

Michael Small focuses on Complex network, Series, Algorithm, Dynamical systems theory and Time series. His Complex network research incorporates elements of Node, Theoretical computer science, Statistical physics and Cluster analysis. His Series study combines topics from a wide range of disciplines, such as Chaotic, Chaos theory, Probability and statistics, Embedding and Noise.

His research integrates issues of Attractor and Topology in his study of Chaotic. His Algorithm research is multidisciplinary, relying on both Term, Stochastic process, Statistics and Nonlinear system. His Dynamical systems theory research also works with subjects such as

  • Surrogate data which connect with Correlation dimension, Linear system and Mathematical optimization,
  • Phase space that intertwine with fields like Betweenness centrality.

His most cited work include:

  • Complex network from pseudoperiodic time series: Topology versus Dynamics (544 citations)
  • Superfamily phenomena and motifs of networks induced from time series (366 citations)
  • RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS (267 citations)

What are the main themes of his work throughout his whole career to date?

Michael Small mainly focuses on Complex network, Algorithm, Series, Nonlinear system and Statistical physics. The various areas that Michael Small examines in his Complex network study include Node, Dynamical systems theory, Theoretical computer science and Topology. His research in Algorithm intersects with topics in Measure, Null hypothesis and Noise.

He combines subjects such as Embedding, Chaotic and Time series with his study of Series. His work carried out in the field of Chaotic brings together such families of science as Attractor and Control theory. As a part of the same scientific family, he mostly works in the field of Statistical physics, focusing on Artificial intelligence and, on occasion, Pattern recognition.

He most often published in these fields:

  • Complex network (39.32%)
  • Algorithm (23.26%)
  • Series (20.30%)

What were the highlights of his more recent work (between 2018-2021)?

  • Complex network (39.32%)
  • Reservoir computing (6.55%)
  • Algorithm (23.26%)

In recent papers he was focusing on the following fields of study:

His primary scientific interests are in Complex network, Reservoir computing, Algorithm, Theoretical computer science and Nonlinear system. His Complex network study integrates concerns from other disciplines, such as Mechanism, Bifurcation, Tree and Function, Topology. His Algorithm research includes elements of Embedding, Markov process and Permutation.

His work deals with themes such as Statistical physics and Information Criteria, which intersect with Nonlinear system. Series and Markov model are commonly linked in his work. His Signal study deals with Chaotic intersecting with Attractor.

Between 2018 and 2021, his most popular works were:

  • Sensitization to immune checkpoint blockade through activation of a STAT1/NK axis in the tumor microenvironment (40 citations)
  • Sensitization to immune checkpoint blockade through activation of a STAT1/NK axis in the tumor microenvironment (40 citations)
  • Synchronization of chaotic systems and their machine-learning models. (39 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Quantum mechanics
  • Artificial intelligence

His primary areas of study are Complex network, Synchronization, Multivariate statistics, Algorithm and Measure. His Complex network research includes themes of Theoretical computer science, Tree, Focus, Data science and Mechanism. Michael Small has researched Synchronization in several fields, including Chaotic systems, Synchronism, Scalar and Control theory.

His Algorithm study incorporates themes from Embedding, Topological conjugacy and Markov process, Markov model. The study incorporates disciplines such as Chaotic, Attractor, Ergodicity, Transformation and Series in addition to Dynamical system. In his study, Time series is strongly linked to Statistical physics, which falls under the umbrella field of Ergodicity.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Complex network from pseudoperiodic time series: Topology versus Dynamics

J. Zhang;Michael Small.
Physical Review Letters (2006)

723 Citations

Applied Nonlinear Time Series Analysis: Applications in Physics, Physiology and Finance

Michael Small.
(2005)

483 Citations

Superfamily phenomena and motifs of networks induced from time series

Xiaoke Xu;Jie Zhang;Michael Small.
Proceedings of the National Academy of Sciences of the United States of America (2008)

471 Citations

RECURRENCE-BASED TIME SERIES ANALYSIS BY MEANS OF COMPLEX NETWORK METHODS

Reik V. Donner;Michael Small;Jonathan F. Donges;Jonathan F. Donges;Norbert Marwan.
International Journal of Bifurcation and Chaos (2011)

371 Citations

Epidemic dynamics on scale-free networks with piecewise linear infectivity and immunization.

Xinchu Fu;Michael Small;David M. Walker;Haifeng Zhang.
Physical Review E (2008)

244 Citations

Complex network analysis of time series

Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths.
EPL (2016)

212 Citations

Surrogate Test for Pseudoperiodic Time Series Data

Michael Small;Michael Small;Dejin Yu;Robert G. Harrison.
Physical Review Letters (2001)

198 Citations

Characterizing pseudoperiodic time series through the complex network approach

Jie Zhang;Junfeng Sun;Xiaodong Luo;Kai Zhang.
Physica D: Nonlinear Phenomena (2008)

188 Citations

The impact of awareness on epidemic spreading in networks

Qingchu Wu;Xinchu Fu;Michael Small;Xin-Jian Xu.
Chaos (2012)

177 Citations

Hub nodes inhibit the outbreak of epidemic under voluntary vaccination

Haifeng Zhang;Haifeng Zhang;Haifeng Zhang;Jie Zhang;Changsong Zhou;Michael Small.
New Journal of Physics (2010)

136 Citations

Best Scientists Citing Michael Small

Jürgen Kurths

Jürgen Kurths

Potsdam Institute for Climate Impact Research

Publications: 74

Zhong-Ke Gao

Zhong-Ke Gao

Tianjin University

Publications: 63

Guanrong Chen

Guanrong Chen

City University of Hong Kong

Publications: 33

Kazuyuki Aihara

Kazuyuki Aihara

University of Tokyo

Publications: 29

Jinde Cao

Jinde Cao

Southeast University

Publications: 28

Hai-Feng Zhang

Hai-Feng Zhang

Chinese Academy of Sciences

Publications: 27

Luonan Chen

Luonan Chen

Chinese Academy of Sciences

Publications: 27

Yong Deng

Yong Deng

Southwest University

Publications: 25

Chi K. Tse

Chi K. Tse

City University of Hong Kong

Publications: 25

Tao Zhou

Tao Zhou

University of Electronic Science and Technology of China

Publications: 20

Bing-Hong Wang

Bing-Hong Wang

University of Science and Technology of China

Publications: 18

Luis A. Aguirre

Luis A. Aguirre

Universidade Federal de Minas Gerais

Publications: 17

Raman Sujith

Raman Sujith

Indian Institute of Technology Madras

Publications: 15

Osvaldo A. Rosso

Osvaldo A. Rosso

National Scientific and Technical Research Council

Publications: 15

Matjaz Perc

Matjaz Perc

University of Maribor

Publications: 13

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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