World's Best Scientists 2026 revealed!

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Computer Science

D-Index
54
Citations
11639
World Ranking
4567
National Ranking
133

Overview

Michael Small is affiliated with the University of Western Australia in Australia. Their research spans the intersection of computer science and physics, with a substantial focus on areas such as statistical and nonlinear physics as well as artificial intelligence.

The main fields of study in their work include:

  • Computer Science
  • Physics and Astronomy

Within these fields, key subfields addressed by Michael Small are:

  • Statistical and Nonlinear Physics
  • Artificial Intelligence
  • Cognitive Neuroscience
  • Computer Networks and Communications
  • Molecular Biology

Their primary research topics cover:

  • Complex Network Analysis Techniques
  • Neural Networks and Reservoir Computing
  • Neural dynamics and brain function
  • Opinion Dynamics and Social Influence
  • Nonlinear Dynamics and Pattern Formation
  • Neural Networks and Applications
  • Chaos control and synchronization

Michael Small has published extensively, with frequent appearances in key venues such as:

  • Chaos An Interdisciplinary Journal of Nonlinear Science
  • arXiv (Cornell University)
  • Physica A Statistical Mechanics and its Applications
  • Physical review. E
  • SSRN Electronic Journal

Recent papers by Michael Small include:

  • Modelling Strong Control Measures for Epidemic Propagation With Networks-A COVID-19 Case Study, 2020, IEEE Access
  • Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology, 2023, Chaos An Interdisciplinary Journal of Nonlinear Science
  • Searching for Key Cycles in a Complex Network, 2023, Physical Review Letters
  • A social communication model based on simplicial complexes, 2020, Physics Letters A
  • Growing networks with communities: A distributive link model, 2020, Chaos An Interdisciplinary Journal of Nonlinear Science

In their collaborative work, Michael Small frequently coauthors with:

  • Shannon D. Algar (18 collaborations)
  • Débora Corrêa (17 collaborations)
  • Thomas Stemler (17 collaborations)
  • Thomas Jüngling (15 collaborations)
  • Thomas Lymburn (11 collaborations)

Best Publications

  • Complex network from pseudoperiodic time series: Topology versus Dynamics

    J. Zhang;Michael Small

  • Superfamily phenomena and motifs of networks induced from time series

    Xiaoke Xu;Jie Zhang;Michael Small

  • Applied nonlinear time series analysis : applications in physics, physiology and finance

    Michael Small

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

    Reik V. Donner;Michael Small;Jonathan F. Donges;Jonathan F. Donges;Norbert Marwan

  • Complex network analysis of time series

    Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths

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

    Xinchu Fu;Michael Small;David M. Walker;Haifeng Zhang

  • The impact of awareness on epidemic spreading in networks

    Qingchu Wu;Xinchu Fu;Michael Small;Xin-Jian Xu

  • Dynamic versus static biomarkers in cancer immune checkpoint blockade: unravelling complexity.

    W. Joost Lesterhuis;Anthony Bosco;Michael J. Millward;Michael J. Millward;Michael Small;Michael Small

  • Time lagged ordinal partition networks for capturing dynamics of continuous dynamical systems.

    Michael McCullough;Michael Small;Thomas Stemler;Herbert Ho-Ching Iu

  • Characterizing pseudoperiodic time series through the complex network approach

    Jie Zhang;Junfeng Sun;Xiaodong Luo;Kai Zhang

  • Surrogate Test for Pseudoperiodic Time Series Data

    Michael Small;Michael Small;Dejin Yu;Robert G. Harrison

  • Hub nodes inhibit the outbreak of epidemic under voluntary vaccination

    Haifeng Zhang;Haifeng Zhang;Haifeng Zhang;Jie Zhang;Changsong Zhou;Michael Small

  • Basin of Attraction Determines Hysteresis in Explosive Synchronization

    Yong Zou;Yong Zou;Tiago Pereira;Michael Small;Zonghua Liu

  • Synchronization of chaotic systems and their machine-learning models.

    Tongfeng Weng;Huijie Yang;Changgui Gu;Jie Zhang

  • Optimal embedding parameters: A modelling paradigm

    Michael Small;Chi Kong Tse

  • Efficient implementation of the gaussian kernel algorithm in estimating invariants and noise level from noisy time series data

    Dejin Yu;Michael Small;Robert G. Harrison;Cees Diks

  • Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep

    Michael Small;Kevin Judd;M. Lowe;S. Stick

  • Super-spreaders and the rate of transmission of the SARS virus

    Michael Small;Chi Kong Tse;David M. Walker

  • Applying the method of surrogate data to cyclic time series

    Michael Small;C.K. Tse

  • Complex network structure of musical compositions: Algorithmic generation of appealing music

    Xiao Fan Liu;Chi Kong Tse;Michael Small

  • Detecting determinism in time series: the method of surrogate data

    M. Small;C.K. Tse

Frequent Co-Authors

Chi K. Tse
Chi K. Tse City University of Hong Kong
Hai-Feng Zhang
Hai-Feng Zhang Chinese Academy of Sciences
Guanrong Chen
Guanrong Chen City University of Hong Kong
Pan Hui
Pan Hui Hong Kong University of Science and Technology
Herbert Ho-Ching Iu
Herbert Ho-Ching Iu University of Western Australia
Jun-an Lu
Jun-an Lu Wuhan University
Hugh P. C. Robinson
Hugh P. C. Robinson University of Cambridge
Bing-Hong Wang
Bing-Hong Wang University of Science and Technology of China
Jürgen Kurths
Jürgen Kurths Potsdam Institute for Climate Impact Research
Wenwu Yu
Wenwu Yu Southeast University

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