Special Issue "Information Theory in Complex Networks"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (30 November 2020).

Special Issue Editors

Dr. Sergi Valverde
E-Mail Website
Guest Editor
Institute of Evolutionary Biology, Pompeu Fabra University, 08002 Barcelona, Spain
Interests: complex systems; network science; evolution of technology; natural computation; artificial intelligence
Dr. Harold Fellerman
E-Mail Website
Guest Editor
School of Computing, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: complex systems; synthetic biology; systems chemistry; DNA nanotechnology

Special Issue Information

Dear Colleagues,

Many complex systems are described by a network of interactions between their often-large number of components. Beyond the specific nature of each system, they are often characterized by a number of universal features. Two recurrent patterns are a heterogeneous distribution of connectivity and the average shortest path lengths connecting any pair of nodes. Another important trait is the hierarchical–modular organisation of many biological and artificial systems. Such structural patterns have been linked to adaptive, functional traits like enhanced robustness against external perturbations or efficient information processing.

Information theory opens a promising roadmap to quantify the complexity of networks. This is particularly relevant when solving inference problems in complex networks, i.e., how to extract the information encoded in network topologies. On the other hand, it has also been proposed that complex structures emerge from evolutionary mechanisms subject to diverse constraints. The application of information theory might help to identify both driving mechanisms as well as structural constraints.

This Special Issue aims to present the latest developments in information theory of complex networks. Contributions applying or extending key concepts from information theory to the analysis of complex networks are very welcome. In particular, both theoretical models and the empirical analysis of real-world natural and artificial complex networks using information theory fall within the scope of this Special issue.

Dr. Sergi Valverde
Dr. Harold Fellerman
Guest Editors

Manuscript Submission Information

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. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short 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 thoroughly refereed through a single-blind 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 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • Complex networks
  • Information theory
  • Statistical physics
  • Evolution
  • Criticality
  • Robustness
  • Natural, sociotechnical and artificial networks
  • Dynamics on and dynamics of networks
  • Information flow in and information processing by networks
  • Information based data analysis and inference

Published Papers (1 paper)

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Inferring Networks of Interdependent Labor Skills to Illuminate Urban Economic Structure
Entropy 2020, 22(10), 1078; https://doi.org/10.3390/e22101078 - 25 Sep 2020
Cited by 2 | Viewed by 877
Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of [...] Read more.
Cities are among the best examples of complex systems. The adaptive components of a city, such as its people, firms, institutions, and physical structures, form intricate and often non-intuitive interdependencies with one another. These interdependencies can be quantified and represented as links of a network that give visibility to otherwise cryptic structural elements of urban systems. Here, we use aspects of information theory to elucidate the interdependence network among labor skills, illuminating parts of the hidden economic structure of cities. Using pairwise interdependencies we compute an aggregate, skills-based measure of system “tightness” of a city’s labor force, capturing the degree of integration or internal connectedness of a city’s economy. We find that urban economies with higher tightness tend to be more productive in terms of higher GDP per capita. However, related work has shown that cities with higher system tightness are also more negatively affected by shocks. Thus, our skills-based metric may offer additional insights into a city’s resilience. Finally, we demonstrate how viewing the web of interdependent skills as a weighted network can lead to additional insights about cities and their economies. Full article
(This article belongs to the Special Issue Information Theory in Complex Networks)
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