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Journal of Physics: Complexity is a peer-reviewed journal published by IOP Publishing, covering Complex Network Analysis Techniques, Opinion Dynamics and Social Influence, Nonlinear Dynamics and Pattern Formation. Over its lifetime it has published 441 papers, which have received 2,405 citations.
| Publisher | IOP Publishing |
|---|---|
| Reference style | Numbered (IOP) Numbered — [1], [2] in the text [1] Smith A, Jones B and Lee C 2023 A representative article title Journal of Physics: Complexity 12 45–58
Formats any DOI in Journal of Physics: Complexity style. No sign-up. |
| Publishes research in | Complex Network Analysis Techniques Opinion Dynamics and Social Influence Nonlinear Dynamics and Pattern Formation Complex Systems and Time Series Analysis Neural dynamics and brain function |
| ISSN | 2632-072X |
| Citation impact (2-yr) | 2.05 |
| h-index | 22 |
| i10-index | 63 |
| Article processing charge | $1,962 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Universidade de São Paulo |
| Journal website | iopscience.iop.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Abstract Simplicial complexes constitute the underlying topology of interacting complex systems including among the others brain and social interaction networks. They are generalized network structures that allow to go beyond the framework of pairwise interactions and to capture the many-body interactions between two or more nodes strongly affecting dynamical processes. In fact, the simplicial complexes…
Abstract Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the…
Abstract Chimera states, states of coexistence of synchronous and asynchronous motion, have been a subject of extensive research since they were first given a name in 2004. Increased interest has lead to their discovery in ever new settings, both theoretical and experimental. Less well-discussed is the fact that successive results have also broadened the notion…
Abstract An emerging paradigm for predicting the state evolution of chaotic systems is machine learning with reservoir computing, the core of which is a dynamical network of artificial neurons. Through training with measured time series, a reservoir machine can be harnessed to replicate the evolution of the target chaotic system for some amount of time,…
Abstract Neuroscience is home to concepts and theories with roots in a variety of domains including information theory, dynamical systems theory, and cognitive psychology. Not all of those can be coherently linked, some concepts are incommensurable, and domain-specific language poses an obstacle to integration. Still, conceptual integration is a form of understanding that provides intuition…
