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Algorithms
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Algorithms in Complex Networks
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Algorithms in Complex Networks

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: closed (15 September 2022) | Viewed by 10003

Special Issue Editors

Prof. Dr. Wanquan Liu

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Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Interests: large-scale pattern recognition; signal processing; machine learning; control systems
Special Issues, Collections and Topics in MDPI journals
Dr. Huafeng Wang

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Guest Editor
School of Information Technology, North China University of Technology, Beijing 100144, China
Interests: pattern recognition; computer vision
Prof. Dr. Xiaojun Tan

E-Mail Website1 Website2
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-sen University. Room.628, Block 1 of Engineering Building, #66 Gongchang Rd., Shenzhen 518107, China
Interests: new energy vehicles; smart driving; cluster control for unmanned systems application; computer vision and its application technology
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengping Fan

E-Mail Website
Guest Editor
School of Intelligent Systems Engineering, Sun Yat-Sen University, Guangzhou 510275 China
Interests: social network; intelligent learning; system control; network information dissemination and application

Special Issue Information

Dear Colleagues,

Many real systems, including the Internet, bio-molecular systems, and economic systems, can be abstracted to networks represented by nodes and links. For example, in the Internet network, nodes stand for routers and links are fiber optic or wireless connections between routers. To analyze the topological characteristics of such networks—sometimes called graphs in mathematical community—numerous algorithms have been proposed in recent years. Typical examples are the Dijkstra algorithm for distance calculation between pair of nodes, the clique percolation algorithm for community detection, and the k-core algorithm for hierarchical decomposition. Although there are enormous algorithms that have been designed by both academic and industry researchers to date, new algorithms and strategies for studying the topological characteristics and dynamical behaviors of networks are still urgently needed—mainly due to the growing network scale and complexity of dynamical behaviors. This Special Issue plans to give an overview of the most recent advances in the field of complex networks. This Special Issue is aimed at providing selected contributions on advances in detecting the topological characteristics of complex networks, modeling the structures and functional behaviors of complex networks, control and synchronization of complex networks, and applications of network-based approaches and algorithms.

Finally, I would like to thank Dr. Tao Du and his valuable work for assisting us with this Special Issue.

Prof. Dr. Wanquan Liu
Dr. Huafeng Wang
Dr. Xiaojun Tan
Dr. Zhengping Fan
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 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. Algorithms 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

  • structure and functional analysis of real networks
  • percolation theory of complex networks
  • dynamical behaviors of networks
  • network control and synchronization
  • network big data computation
  • applications of network-based approaches and algorithms
  • machine learning based algorithms for dynamic networks

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Published Papers (4 papers)

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25 pages, 2715 KiB  
Article
Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
by Yuanzhi Huo, Pradini Puspitaningayu, Nobuo Funabiki, Kazushi Hamazaki, Minoru Kuribayashi, Yihan Zhao and Kazuyuki Kojima
Algorithms 2023, 16(1), 45; https://doi.org/10.3390/a16010045 - 9 Jan 2023
Cited by 2 | Viewed by 1762
Abstract
Parameters often take key roles in determining the accuracy of algorithms, logics, and models for practical applications. Previously, we have proposed a general-purpose parameter optimization algorithm, and studied its applications in various practical problems. This algorithm optimizes the parameter values by repeating [...] Read more.
Parameters often take key roles in determining the accuracy of algorithms, logics, and models for practical applications. Previously, we have proposed a general-purpose parameter optimization algorithm, and studied its applications in various practical problems. This algorithm optimizes the parameter values by repeating small changes of them based on a local search method with hill-climbing capabilities. In this paper, we present three diverse applications of this algorithm to show the versatility and effectiveness. The first application is the fingerprint-based indoor localization system using IEEE802.15.4 devices called FILS15.4 that can detect the location of a user in an indoor environment. It is shown that the number of fingerprints for each detection point, the fingerprint values, and the detection interval are optimized together, and the average detection accuracy exceeds 99%. The second application is the human face contour approximation model that is described by a combination of half circles, line segments, and a quadratic curve. It is shown that the simple functions can well approximate the face contour of various persons by optimizing the center coordinates, radii, and coefficients. The third application is the computational fluid dynamic (CFD) simulation to estimate temperature changes in a room. It is shown that the thermal conductivity is optimized to make the average temperature difference between the estimated and measured 0.22∘C. Full article
(This article belongs to the Special Issue Algorithms in Complex Networks)
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20 pages, 3279 KiB  
Article
Corner Centrality of Nodes in Multilayer Networks: A Case Study in the Network Analysis of Keywords
by Rosa María Rodriguez-Sánchez and Jorge Chamorro-Padial
Algorithms 2022, 15(10), 336; https://doi.org/10.3390/a15100336 - 20 Sep 2022
Cited by 1 | Viewed by 1887
Abstract
In this paper, we present a new method to measure the nodes’ centrality in a multilayer network. The multilayer network represents nodes with different relations between them. The nodes have an initial relevance or importance value. Then, the node’s centrality is obtained according [...] Read more.
In this paper, we present a new method to measure the nodes’ centrality in a multilayer network. The multilayer network represents nodes with different relations between them. The nodes have an initial relevance or importance value. Then, the node’s centrality is obtained according to this relevance along with its relationship to other nodes. Many methods have been proposed to obtain the node’s centrality by analyzing the network as a whole. In this paper, we present a new method to obtain the centrality in which, in the first stage, every layer would be able to define the importance of every node in the multilayer network. In the next stage, we would integrate the importance given by each layer to each node. As a result, the node that is perceived with a high level of importance for all of its layers, and the neighborhood with the highest importance, obtains the highest centrality score. This score has been named the corner centrality. As an example of how the new measure works, suppose we have a multilayer network with different layers, one per research area, and the nodes are authors belonging to an area. The initial importance of the nodes (authors) could be their h-index. A paper published by different authors generates a link between them in the network. The authors can be in the same research area (layer) or different areas (different layers). Suppose we want to obtain the centrality measure of the authors (nodes) in a concrete area (target layer). In the first stage, every layer (area) receives the importance of every node in the target layer. Additionally, in the second stage, the relative importance given for every layer to every node is integrated with the importance of every node in its neighborhood in the target layer. This process can be repeated with every layer in the multilayer network. The method proposed has been tested with different configurations of multilayer networks, with excellent results. Moreover, the proposed algorithm is very efficient regarding computational time and memory requirements. Full article
(This article belongs to the Special Issue Algorithms in Complex Networks)
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24 pages, 577 KiB  
Article
Scale-Free Random SAT Instances
by Carlos Ansótegui , Maria Luisa Bonet and Jordi Levy
Algorithms 2022, 15(6), 219; https://doi.org/10.3390/a15060219 - 20 Jun 2022
Cited by 1 | Viewed by 2472
Abstract
We focus on the random generation of SAT instances that have properties similar to real-world instances. It is known that many industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. [...] Read more.
We focus on the random generation of SAT instances that have properties similar to real-world instances. It is known that many industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. This is not possible, in general, with classical randomly generated instances. We provide a different generation model of SAT instances, called scale-free random SAT instances. This is based on the use of a non-uniform probability distribution P(i)∼i−β to select variable i, where β is a parameter of the model. This results in formulas where the number of occurrences k of variables follows a power-law distribution P(k)∼k−δ, where δ=1+1/β. This property has been observed in most real-world SAT instances. For β=0, our model extends classical random SAT instances. We prove the existence of a SAT–UNSAT phase transition phenomenon for scale-free random 2-SAT instances with β<1/2 when the clause/variable ratio is m/n=1−2β(1−β)2. We also prove that scale-free random k-SAT instances are unsatisfiable with a high probability when the number of clauses exceeds ω(n(1−β)k). The proof of this result suggests that, when β>1−1/k, the unsatisfiability of most formulas may be due to small cores of clauses. Finally, we show how this model will allow us to generate random instances similar to industrial instances, of interest for testing purposes. Full article
(This article belongs to the Special Issue Algorithms in Complex Networks)
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19 pages, 2123 KiB  
Article
SentenceLDA- and ConNetClus-Based Heterogeneous Academic Network Analysis for Publication Ranking
by Jinsong Zhang, Bao Jin, Junyi Sha, Yan Chen and Yijin Zhang
Algorithms 2022, 15(5), 159; https://doi.org/10.3390/a15050159 - 10 May 2022
Viewed by 1985
Abstract
Scientific papers published in journals or conferences, also considered academic publications, are the manifestation of scientific research achievements. Lots of scientific papers published in digital form bring new challenges for academic evaluation and information retrieval. Therefore, research on the ranking method of scientific [...] Read more.
Scientific papers published in journals or conferences, also considered academic publications, are the manifestation of scientific research achievements. Lots of scientific papers published in digital form bring new challenges for academic evaluation and information retrieval. Therefore, research on the ranking method of scientific papers is significant for the management and evaluation of academic resources. In this paper, we first identify internal and external factors for evaluating scientific papers and propose a publication ranking method based on an analysis of a heterogeneous academic network. We use four types of metadata (i.e., author, venue (journal or conference), topic, and title) as vertexes for creating the network; in there, the topics are trained by the SentenceLDA algorithm with the metadata of the abstract. We then use the Gibbs sampling method to create a heterogeneous academic network and apply the ConNetClus algorithm to calculate the probability value of publication ranking. To evaluate the significance of the method proposed in this paper, we compare the ranking results with BM25, PageRank, etc., and homogeneous networks in MAP and NDCG. As shown in our evaluation results, the performance of the method we propose in this paper is better than other baselines for ranking publications. Full article
(This article belongs to the Special Issue Algorithms in Complex Networks)
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