Special Issue "Computation in Complex Networks"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Dr. Clara Pizzuti Website E-Mail
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Interests: evolutionary computation; complex network analysis and mining; data mining; data streams
Guest Editor
Dr. Annalisa Socievole E-Mail
National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036 Rende (CS), Italy
Interests: complex networks; community detection; evolutionary computation; network robustness

Special Issue Information

Dear Colleagues,

Complex networks, in recent years, are increasingly attracting the attention of researchers from many different domains, such as physics, mathematics, biology, medicine, engineering, and computer science, among others. Complex networks are able to model a wide variety of structures that support the functioning of daily life, including high technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. Understanding how complex systems behave is thus an imperative for many different fields due to their interwoven and multidisciplinary nature and inherent complexity.

This Special Issue aims at collecting original and high-quality papers within the research field of complex network computation. When investigating complex systems, several relevant questions arise such as how information/viruses spread, how groups of nodes/diseases form and evolve, and how to detect and improve robustness over their non-trivial topological structure, just to provide some examples. Papers analyzing transportation infrastructures, communication networks, financial networks, political networks, power grid systems, ecosystems, bioinformatics and network medicine aspects are welcome. We also invite papers conceptualizing complex systems through theoretical frameworks.

Prof. Dr. Clara Pizzuti
Dr. Annalisa Socievole
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 1600 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

  • Community discovery in complex networks
  • Motif discovery in complex networks
  • Link prediction in complex networks
  • Anomaly detection in complex networks
  • Complex networks modeling and analysis
  • Multilayer, temporal and heterogeneous networks
  • Information spreading in complex networks
  • Social influence, reputation and trust in complex networks
  • Visual representation of complex networks
  • Epidemics in complex networks
  • Cascading failures in complex networks
  • Attack vulnerability, resilience and robustness in complex networks
  • Political networks
  • Financial networks
  • Complex networks for IoT, smart cities and smart grids
  • Network medicine
  • Mobile complex networks

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture
Entropy 2019, 21(9), 891; https://doi.org/10.3390/e21090891 (registering DOI) - 14 Sep 2019
Abstract
With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to [...] Read more.
With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
Open AccessArticle
Minimum Memory-Based Sign Adjustment in Signed Social Networks
Entropy 2019, 21(8), 728; https://doi.org/10.3390/e21080728 - 25 Jul 2019
Abstract
In social networks comprised of positive (P) and negative (N) symmetric relations, individuals (nodes) will, under the stress of structural balance, alter their relations (links or edges) with their neighbours, either from positive to negative or vice versa. In the real world, individuals [...] Read more.
In social networks comprised of positive (P) and negative (N) symmetric relations, individuals (nodes) will, under the stress of structural balance, alter their relations (links or edges) with their neighbours, either from positive to negative or vice versa. In the real world, individuals can only observe the influence of their adjustments upon the local balance of the network and take this into account when adjusting their relationships. Sometime, their local adjustments may only respond to their immediate neighbourhoods, or centre upon the most important neighbour. To study whether limited memory affects the convergence of signed social networks, we introduce a signed social network model, propose random and minimum memory-based sign adjustment rules, and analyze and compare the impacts of an initial ratio of positive links, rewire probability, network size, neighbor number, and randomness upon structural balance under these rules. The results show that, with an increase of the rewiring probability of the generated network and neighbour number, it is more likely for the networks to globally balance under the minimum memory-based adjustment. While the Newmann-Watts small world model (NW) network becomes dense, the counter-intuitive phenomena emerges that the network will be driven to a global balance, even under the minimum memory-based local sign adjustment, no matter the network size and initial ratio of positive links. This can help to manage and control huge networks with imited resources. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
Show Figures

Figure 1

Open AccessArticle
A SOM-Based Membrane Optimization Algorithm for Community Detection
Entropy 2019, 21(5), 533; https://doi.org/10.3390/e21050533 - 25 May 2019
Abstract
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. [...] Read more.
The real world is full of rich and valuable complex networks. Community structure is an important feature in complex networks, which makes possible the discovery of some structure or hidden related information for an in-depth study of complex network structures and functional characteristics. Aimed at community detection in complex networks, this paper proposed a membrane algorithm based on a self-organizing map (SOM) network. Firstly, community detection was transformed as discrete optimization problems by selecting the optimization function. Secondly, three elements of the membrane algorithm, objects, reaction rules, and membrane structure were designed to analyze the properties and characteristics of the community structure. Thirdly, a SOM was employed to determine the number of membranes by learning and mining the structure of the current objects in the decision space, which is beneficial to guiding the local and global search of the proposed algorithm by constructing the neighborhood relationship. Finally, the simulation experiment was carried out on both synthetic benchmark networks and four real-world networks. The experiment proved that the proposed algorithm had higher accuracy, stability, and execution efficiency, compared with the results of other experimental algorithms. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
Show Figures

Figure 1

Open AccessArticle
Image Entropy for the Identification of Chimera States of Spatiotemporal Divergence in Complex Coupled Maps of Matrices
Entropy 2019, 21(5), 523; https://doi.org/10.3390/e21050523 - 24 May 2019
Abstract
Complex networks of coupled maps of matrices (NCMM) are investigated in this paper. It is shown that a NCMM can evolve into two different steady states—the quiet state or the state of divergence. It appears that chimera states of spatiotemporal divergence do exist [...] Read more.
Complex networks of coupled maps of matrices (NCMM) are investigated in this paper. It is shown that a NCMM can evolve into two different steady states—the quiet state or the state of divergence. It appears that chimera states of spatiotemporal divergence do exist in the regions around the boundary lines separating these two steady states. It is demonstrated that digital image entropy can be used as an effective measure for the visualization of these regions of chimera states in different networks (regular, feed-forward, random, and small-world NCMM). Full article
(This article belongs to the Special Issue Computation in Complex Networks)
Show Figures

Figure 1

Open AccessArticle
Evolution Model of Spatial Interaction Network in Online Social Networking Services
Entropy 2019, 21(4), 434; https://doi.org/10.3390/e21040434 - 24 Apr 2019
Abstract
The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, [...] Read more.
The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities. Full article
(This article belongs to the Special Issue Computation in Complex Networks)
Show Figures

Figure 1

Back to TopTop