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Axioms
Special Issues
Modeling and Analysis of Complex Network
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Modeling and Analysis of Complex Network

A special issue of Axioms (ISSN 2075-1680).

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 1900

Special Issue Editor

Dr. Letizia Milli

E-Mail Website
Guest Editor
Computer Science Department, University of Pisa, Largo Bruno Pontecorvo 3, 56124 Pisa, Italy
Interests: dynamics on complex networks; complex networks; quantification

Special Issue Information

Dear Colleagues,

I am excited to launch a new Special Issue of Axioms. 

Network science, network analysis, and network mining are new scientific topics that have attracted much attention among researchers. Instead of studying entities' properties, network science focuses on the interaction between these entities. The large quantity of available relational data (online social networks, cell phones, the Internet and the Web, trip datasets, etc.) encourages new research on the topic.

The central topic in this Special Issue will be "Modeling and Analysis of Complex Network".

We aim to showcase recent contributions in the many branches of theoretical and practical studies in complex analysis and its extensions and generalizations. 

Among the topics that this Special Issue will address, we may consider the following non-exhaustive list: models of complex networks; dynamics on and of complex networks; algorithms for network analysis; complex networks mining; structural network properties; community structure and discovery; information spreading in social media. The Special Issue is open to receiving further related topics of complex analysis.

We highly encourage you to submit your original research papers for inclusion in the Special Issue.

Dr. Letizia Milli
Guest Editor

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 submissions that pass pre-check are 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. Axioms 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 2400 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.

Keywords

  • models of complex networks
  • dynamics on and of complex networks
  • algorithms for network analysis
  • complex networks mining
  • structural network properties
  • community structure and discovery
  • information spreading in social media

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Published Papers (1 paper)

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Research

19 pages, 511 KiB  
Article
Modeling and Analysis of Monkeypox Outbreak Using a New Time Series Ensemble Technique
by Wilfredo Meza Cuba, Juan Carlos Huaman Alfaro, Hasnain Iftikhar and Javier Linkolk López-Gonzales
Axioms 2024, 13(8), 554; https://doi.org/10.3390/axioms13080554 - 14 Aug 2024
Cited by 1 | Viewed by 1540
Abstract
The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox [...] Read more.
The coronavirus pandemic has raised concerns about the emergence of other viral infections, such as monkeypox, which has become a significant hazard to public health. Thus, this work proposes a novel time series ensemble technique for analyzing and forecasting the spread of monkeypox in the four highly infected countries with the monkeypox virus. This approach involved processing the first cumulative confirmed case time series to address variance stabilization, normalization, stationarity, and a nonlinear secular trend component. After that, five single time series models and three proposed ensemble models are used to estimate the filtered confirmed case time series. The accuracy of the models is evaluated using typical accuracy mean errors, graphical evaluation, and an equal forecasting accuracy statistical test. Based on the results, it is found that the proposed time series ensemble forecasting approach is an efficient and accurate way to forecast the cumulative confirmed cases for the top four countries in the world and the entire world. Using the best ensemble model, a forecast is made for the next 28 days (four weeks), which will help understand the spread of the disease and the associated risks. This information can prevent further spread and enable timely and effective treatment. Furthermore, the developed novel time series ensemble approach can be used to forecast other diseases in the future. Full article
(This article belongs to the Special Issue Modeling and Analysis of Complex Network)
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