Special Issue "Recent Advances in Chaos Theory and Complex Networks"


A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 30 April 2015

Special Issue Editors

Guest Editor
Prof. Dr. Guanrong Chen
Department of Electronic Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, China
Website: http://www.ee.cityu.edu.hk/~gchen/
E-Mail: eegchen@cityu.edu.hk
Phone: +852 3442 7922
Interests: chaos; complex networks; dynamical systems; bifurcation; synchronization

Guest Editor
Prof. Dr. C.K. Michael Tse
Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
Website: http://cktse.eie.polyu.edu.hk/Home.html
E-Mail: cktse@ieee.org
Phone: +852 2766 6246
Interests: chaos; power electronics; communications; complex networks applications

Guest Editor
Prof. Dr. Mustak E. Yalcin
Department of Electronics and Communication Engineering, Faculty of Electrical and Electronic Engineering, Istanbul Technical University, Maslak, 34469, Istanbul, Turkey
Website: http://www2.itu.edu.tr/~yalcinmust
E-Mail: mustak.yalcin@itu.edu.tr
Phone: +90 212 285 3612
Interests: nonlinear circuits and systems; neural networks; complex systems; nonlinear dynamics; complex networks

Guest Editor
Dr. Hai Yu
Software College, Northeastern University, Shenyang, Liaoning, P. R. China
Website: http://faculty.neu.edu.cn/yuhai/english.html
E-Mail: yuhai@mail.neu.edu.cn
Phone: +86 24 8368 8871
Interests: chaos; complex networks; communications; video coding; digital chaotic cipher

Guest Editor
Dr. Mattia Frasca
Department of Electrical, Electronic and Computer Science Engineering, University of Catania, Italy
Website: http://www.dees.unict.it/mfrasca/home.html
E-Mail: mattia.frasca@dieei.unict.it
Phone: +39 0957382307
Interests: complex networks; chaotic circuits; memristors

Special Issue Information

Dear Colleagues,

As one of the greatest natural science discoveries in the 20th century, chaos theory has been extensively investigated, and significant progress has been made in the past several decades. It has become an exciting emerging interdisciplinary area in which a broad spectrum of methodologies and technologies are being studied to deal with large, complex, and dynamical problems. In the last decade, the notion of complex networks as part of the complexity studies is actively and intensively investigated in the broad areas of computer and communication networks, physical and biological networks, as well as social and financial networks. In particular, chaotic networks, i.e., networks of chaotic maps, have emerged as a new trend of proactive research, extending the classical subject of coupled map lattices regarding their dynamical properties such as spatiotemporal chaos, bifurcations and entropy.

The main focus of this Special Issue will be on the state-of-the-art advancements in chaos theory and complex networks, as well as their applications to dynamical systems, thermodynamics, information processing, communications, cryptography, systems biology, and so on.

Prof. Dr. Guanrong Chen
Prof. Dr. C.K. Michael Tse
Prof. Dr. Mustak E. Yalcin
Dr. Hai Yu
Dr. Mattia Frasca
Guest Editors


Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs).


  • chaos
  • complex network
  • complexity
  • entropy
  • fractional calculus
  • information theory
  • nonlinearity
  • nonlinear dynamics
  • systems biology

Published Papers (2 papers)

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p. 5618-5632
by ,  and
Entropy 2014, 16(11), 5618-5632; doi:10.3390/e16115618
Received: 23 September 2014; in revised form: 20 October 2014 / Accepted: 22 October 2014 / Published: 27 October 2014
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(This article belongs to the Special Issue Recent Advances in Chaos Theory and Complex Networks)
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p. 5358-5376
by , ,  and
Entropy 2014, 16(10), 5358-5376; doi:10.3390/e16105358
Received: 30 August 2014; in revised form: 5 October 2014 / Accepted: 8 October 2014 / Published: 15 October 2014
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(This article belongs to the Special Issue Recent Advances in Chaos Theory and Complex Networks)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Dynamical Systems from Networks Constructed from Time Series"
Authors: Lvlin Hou 1 and Michael Small 2
Affiliations: 1 National University of Defence Technology, China
2 The University of Western Australia
Abstract: Several methods exist to construct complex networks from time series. In general these methods claim to construct complex networks which preserve certain properties of the underlying time series. In this paper we test this assertion by developing an algorithm to realise dynamical systems from these complex networks in such a way that trajectories of these dynamical systems are time series that preserve certain statistical properties of the original time series (and hence the underlying true dynamical system). Using this algorithm we are able to demonstrate that the so-called $k$-nearet neighbour adaptive algorithm for generating networks out-performs methods based of $\epsilon$-ball recurrence plots. For such networks - and with suitable choice of parameter values, which we provide - the time series generated by this method function as a nonlinear surrogate generation algorithm. Hence, one can use this approach to test the null hypothesis of a noise driven nonlinear periodic orbit in observed time series data - with the alternative hypothesis of deterministic chaos.

Last update: 10 October 2014

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