Computational Complex Networks, 2nd Edition

Topic Information

Dear Colleagues,

Computational methods and models in complex networks have recently proved useful in investigating a variety of networked systems, where diverse network properties can be analyzed. They are widely applied in areas such as network communication, control, prediction, estimation, and security. Recently, fruitful achievements in the research of computation in complex networks have been reported in the literature. At the same time, with the deepening of research, many new problems and challenges have emerged. In particular, fractal theory and fractional calculus have provided powerful tools for characterizing the structural complexity, scaling behaviors, and dynamic processes of complex networks. Incorporating these perspectives can enhance the modeling of network heterogeneity, long‑range dependence, and memory effects. This Topic aims to provide the latest theoretical methods or practical algorithms for complex networks and their applications, including those that utilize fractal or fractional‑order approaches. It is hoped that the contents of this Topic can provide useful information and technical references for readers interested in this area to promote progress in network computation. This Topic has a wide scope, covering contributions from theoretical advances to practical applications.

Dr. Alexandre G. Evsukoff
Dr. Yilun Shang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.1	4.5	2008	19.2 Days	CHF 1800	Submit
Complexities complexities	-	-	2025	15.0 days *	CHF 1000	Submit
Entropy entropy	2.0	5.2	1999	21.5 Days	CHF 2600	Submit
Fractal and Fractional fractalfract	3.3	6.0	2017	19.3 Days	CHF 2700	Submit
Information information	2.9	6.5	2010	20.9 Days	CHF 1800	Submit
Physics physics	1.8	3.1	2019	37.8 Days	CHF 1400	Submit

* Median value for all MDPI journals in the second half of 2025.

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All Algorithms Complexities Entropy Fractal Fract Information Physics

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23 pages, 2933 KB  

Open AccessArticle

Iterative Generation and Generalized Degree Distribution of Higher-Order Fractal Scale-Free Networks

by Lin Qi, Jiaxin Zhang, Ying Fan and Feiyan Guo

Fractal Fract. 2026, 10(5), 306; https://doi.org/10.3390/fractalfract10050306 - 30 Apr 2026

Viewed by 135

Abstract

Fractals represent one of the fundamental manifestations of complexity, and fractal networks serve as tools for characterizing and investigating the fractal structures and properties of large-scale systems. Higher-order networks have emerged as a research hotspot due to their ability to express interactions among [...] Read more.

Fractals represent one of the fundamental manifestations of complexity, and fractal networks serve as tools for characterizing and investigating the fractal structures and properties of large-scale systems. Higher-order networks have emerged as a research hotspot due to their ability to express interactions among multiple nodes. This study proposes an iterative generation model for higher-order fractal networks. The iteration is controlled by three parameters: the dimension $K$ of the simplicial complex, the multiplier $m$, and the iteration count $t$. The constructed network is a pure simplicial complex. Theoretical analysis using the similarity dimension and experimental verification using the box-counting dimension demonstrate that the generated networks exhibit fractal characteristics. When the multiplier $m$ is large, the generalized degree distribution of the generated networks exhibits scale-free properties. Full article

(This article belongs to the Topic Computational Complex Networks, 2nd Edition)

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32 pages, 617 KB  

Open AccessArticle

Analyzing Late Antiquity Shifts of Trade Regime in the Iberian Peninsula and Their Causes via Change Point Detection Methods

by Juan Julián Merelo-Guervós

Complexities 2026, 2(2), 12; https://doi.org/10.3390/complexities2020012 - 16 Apr 2026

Viewed by 274

Abstract

History attempts to make sense of disparate information by trying to create discourse that lays a series of events with crisp cause–effect relationships in a sequence. Epochal shifts, such as the change from Antiquity to the Middle Ages, are especially complex since they [...] Read more.

History attempts to make sense of disparate information by trying to create discourse that lays a series of events with crisp cause–effect relationships in a sequence. Epochal shifts, such as the change from Antiquity to the Middle Ages, are especially complex since they involve a large number of economic, political and even religious factors which occur over long periods and that might overlap and interact through reciprocal feedback mechanisms, making this cause–effects sequence difficult to establish. In this research we adopt a data-driven and well-established methodology to identify, with quantifiable statistical precision, the moment when this shift happened, and from there arrive at its possible causes. We will use historical coin hoard data to find out whether such a shift is detected in a peripheral part of the Roman Empire, the Iberian Peninsula. To do so, we will apply different changepoint analysis methods to a time series of trade links created from that data, and conduct a retrospective analysis based on that result, analyzing the structure of the trade networks before and after the link. Thus, we progress from identifying when the shift happened to identifying where it took place, which in turn allows us to get to investigate why it happened, namely, historical events that could have caused it. This methodology can be used to analyze epochal changes in several steps using time-stamped network data, possibly finding disregarded causes or cause–effect links that could have been overlooked by qualitative methods; in this case, we have applied it to a dataset of coin hoards either found in the Iberian Peninsula or including coins minted there, finding a changepoint in the early 5th century, which, through network analysis, has been linked to a loss of trade with the area of Britannia. Full article

(This article belongs to the Topic Computational Complex Networks, 2nd Edition)

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