Applied Modern Mathematics in Complex Networks

A special issue of Mathematical and Computational Applications (ISSN 2297-8747).

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 44816

Special Issue Editors


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Guest Editor
Department of Computer Science, Chalous Branch, Islamic Azad University, Chalous, Iran
Interests: machine intelligence; reinforcement learning; information forensics and security; data science; big data

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Guest Editor
ICS/5GIC, University of Surrey, Guildford GU27XH, UK
Interests: distributed computing; distributed systems; applied artificial intelligence and mathematics; 5G network; green communications; network and system security
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Guest Editor
SPRITZ Security and Privacy Research Group, University of Padua, 35121 Padova, Italy
Interests: cloud privacy; fog computing; smart grid; sensor network; resource allocation and scheduling
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Special Issue Information

Dear Colleagues,

Modern mathematics is at the heart of an increasing number of innovations in the field of digital communication, satellite navigation, e-commerce, medical technology, and consumer electronics. Without modern mathematics, there would be no navigation system, no mobile phone, no secure payment on the Internet, no digital TV, and no MP3 player. There would also be no efficient web search, no Blu-ray discs, and no digital rights management (DRM). Modeling and simulation of computer and communication networks as well as social network analysis are also based on profound mathematical knowledge and methods. Current and previous developments in these highly innovative fields have led to a new understanding of mathematics. On the other hand, the study of networks is concerned explicitly with connectivity between different entities; it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. Some of the most fascinating problems in applied mathematics today concern the structure and dynamics of systems composed of many interacting network components. Many discrete data sets, and problems in which some kind of interactions or coupling are relevant, benefit from the study of networks, which has become one of the most prominent areas of applied mathematics, physics, computer science, and other disciplines.

In this SI call, the aim is to present the role of modern statistical methods in complex networks, as well as robustness and reliability in complex networks, and serves to support interactions among mathematicians, statisticians, engineers, and scientists working in the interface of experiment, computation, analysis, and statistics. Potential topics include, but are not limited to:

  • Recent advancement in machine learning and shape analysis;
  • Recent advancement in data classification and clustering in complex networks;
  • Recent development in biomedical research;
  • Algorithms for network analysis;
  • Networks, smart cities, and smart grids;
  • Resilience and robustness in complex networks;
  • Large-scale graph analytics;
  • Storage and routing in complex systems;
  • Recommendation systems and complex networks;
  • Applications (including but not limited to physical measurements, modeling energy end-use, vulnerability and sustainability of complex networks, etc.);
  • Security and privacy issues for adopting complex systems.

Dr. Shahaboddin Shamshirband
Dr. Mohammad Shojafar
Dr. Zahra Pooranian
Guest Editors

Manuscript Submission Information

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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. Mathematical and Computational Applications is an international peer-reviewed open access semimonthly 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 1400 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

  • mathematical optimization
  • machine learning
  • complex network
  • graph theory
  • queuing theory
  • smart grids
  • smart cities
  • data mining
  • cost analysis of green networks

Published Papers (4 papers)

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Research

14 pages, 293 KiB  
Article
Dual Methods for Optimal Allocation of Telecommunication Network Resources with Several Classes of Users
by Igor Konnov, Aleksey Kashuba and Erkki Laitinen
Math. Comput. Appl. 2018, 23(2), 31; https://doi.org/10.3390/mca23020031 - 17 Jun 2018
Cited by 2 | Viewed by 2925
Abstract
We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users’ requirements. [...] Read more.
We consider a general problem of optimal allocation of limited resources in a wireless telecommunication network. The network users are divided into several different groups (or classes), which correspond to different levels of service. The network manager must satisfy these different users’ requirements. This approach leads to a convex optimization problem with balance and capacity constraints. We present several decomposition type methods to find a solution to this problem, which exploit its special features. We suggest applying first the dual Lagrangian method with respect to the total capacity constraint, which gives the one-dimensional dual problem. However, calculation of the value of the dual cost function requires solving several optimization problems. Our methods differ in approaches for solving these auxiliary problems. We consider three basic methods: Dual Multi Layer (DML), Conditional Gradient Dual Multilayer (CGDM) and Bisection (BS). Besides these methods we consider their modifications adjusted to different kind of cost functions. Our comparison of the performance of the suggested methods on several series of test problems show satisfactory convergence. Nevertheless, proper decomposition techniques enhance the convergence essentially. Full article
(This article belongs to the Special Issue Applied Modern Mathematics in Complex Networks)
19 pages, 3142 KiB  
Article
The Impact of the Implementation Cost of Replication in Data Grid Job Scheduling
by Babar Nazir, Faiza Ishaq, Shahaboddin Shamshirband and Anthony T. Chronopoulos
Math. Comput. Appl. 2018, 23(2), 28; https://doi.org/10.3390/mca23020028 - 25 May 2018
Cited by 6 | Viewed by 3249
Abstract
Data Grids deal with geographically-distributed large-scale data-intensive applications. Schemes scheduled for data grids attempt to not only improve data access time, but also aim to improve the ratio of data availability to a node, where the data requests are generated. Data replication techniques [...] Read more.
Data Grids deal with geographically-distributed large-scale data-intensive applications. Schemes scheduled for data grids attempt to not only improve data access time, but also aim to improve the ratio of data availability to a node, where the data requests are generated. Data replication techniques manage large data by storing a number of data files efficiently. In this paper, we propose centralized dynamic scheduling strategy-replica placement strategies (CDSS-RPS). CDSS-RPS schedule the data and task so that it minimizes the implementation cost and data transfer time. CDSS-RPS consists of two algorithms, namely (a) centralized dynamic scheduling (CDS) and (b) replica placement strategy (RPS). CDS considers the computing capacity of a node and finds an appropriate location for the job. RPS attempts to improve file access time by using replication on the basis of number of accesses, storage capacity of a computing node, and response time of a requested file. Extensive simulations are carried out to demonstrate the effectiveness of the proposed strategy. Simulation results demonstrate that the replication and scheduling strategies improve the implementation cost and average access time significantly. Full article
(This article belongs to the Special Issue Applied Modern Mathematics in Complex Networks)
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15 pages, 1825 KiB  
Article
Machine Learning-Based Sentiment Analysis for Twitter Accounts
by Ali Hasan, Sana Moin, Ahmad Karim and Shahaboddin Shamshirband
Math. Comput. Appl. 2018, 23(1), 11; https://doi.org/10.3390/mca23010011 - 27 Feb 2018
Cited by 193 | Viewed by 33390
Abstract
Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various [...] Read more.
Growth in the area of opinion mining and sentiment analysis has been rapid and aims to explore the opinions or text present on different platforms of social media through machine-learning techniques with sentiment, subjectivity analysis or polarity calculations. Despite the use of various machine-learning techniques and tools for sentiment analysis during elections, there is a dire need for a state-of-the-art approach. To deal with these challenges, the contribution of this paper includes the adoption of a hybrid approach that involves a sentiment analyzer that includes machine learning. Moreover, this paper also provides a comparison of techniques of sentiment analysis in the analysis of political views by applying supervised machine-learning algorithms such as Naïve Bayes and support vector machines (SVM). Full article
(This article belongs to the Special Issue Applied Modern Mathematics in Complex Networks)
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12 pages, 2233 KiB  
Article
A Fast Recommender System for Cold User Using Categorized Items
by Hamid Jazayeriy, Saghi Mohammadi and Shahaboddin Shamshirband
Math. Comput. Appl. 2018, 23(1), 1; https://doi.org/10.3390/mca23010001 - 15 Jan 2018
Cited by 15 | Viewed by 4462
Abstract
In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user’s time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active [...] Read more.
In recent years, recommender systems (RS) provide a considerable progress to users. RSs reduce the cost of a user’s time in order to reach to desired results faster. The main issue of RSs is the presence of cold users which are less active and their preferences are more difficult to detect. The aim of this study is to provide a new way to improve recall and precision in recommender systems for cold users. According to the available categories of items, prioritization of the proposed items is improved and then presented to the cold user. The obtained results show that in addition to increased speed of processing, recall and precision have an acceptable improvement. Full article
(This article belongs to the Special Issue Applied Modern Mathematics in Complex Networks)
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