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Algorithms
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Network Science: Algorithms and Applications
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Network Science: Algorithms and Applications

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 (16 January 2022) | Viewed by 26348

Special Issue Editor

Dr. Lijun Chang

E-Mail Website
Guest Editor
School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: graph database; graph mining; algorithms; network science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Network science is an active area of research that makes practical sense of networks and has significant applications in diverse scientific fields. The graph structure is a unified data structure for modeling network data, and therefore, many algorithmic techniques have been developed to transform network science problems into graph-theoretic problems in order to deliver effective and efficient solutions. Due to the emergence of new network science applications, the growth of network data sizes, and the dynamically changing nature of networks, there are still numerous challenges to overcome and many research questions to be answered. We invite researchers and practitioners who work with graphs and networks to submit their work to this Special Issue on “Network Science: Algorithms and Applications”. Subjects that will be covered in this Special Issue range from theory to algorithms and applications. A non-exhaustive list of topics of interests is as follows:

  • Novel theoretical foundations in network science
  • Novel models for network science
  • Efficient algorithms for network science
  • Network representation learning
  • Machine learning for network science
  • Dynamic and evolving networks
  • Applications of network science in different disciplines 

Dr. Lijun Chang
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. 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

  • Theoretical foundations for network science 
  • Algorithms for network science 
  • Models for network science 
  • Graph machine learning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

20 pages, 21029 KiB  
Article
An Effective Algorithm for Finding Shortest Paths in Tubular Spaces
by Dang-Viet-Anh Nguyen, Jérôme Szewczyk and Kanty Rabenorosoa
Algorithms 2022, 15(3), 79; https://doi.org/10.3390/a15030079 - 25 Feb 2022
Cited by 1 | Viewed by 2911
Abstract
We propose a novel algorithm to determine the Euclidean shortest path (ESP) from a given point (source) to another point (destination) inside a tubular space. The method is based on the observation data of a virtual particle (VP) assumed to move along this [...] Read more.
We propose a novel algorithm to determine the Euclidean shortest path (ESP) from a given point (source) to another point (destination) inside a tubular space. The method is based on the observation data of a virtual particle (VP) assumed to move along this path. In the first step, the geometric properties of the shortest path inside the considered space are presented and proven. Utilizing these properties, the desired ESP can be segmented into three partitions depending on the visibility of the VP. Our algorithm will check which partition the VP belongs to and calculate the correct direction of its movement, and thus the shortest path will be traced. The proposed method is then compared to Dijkstra’s algorithm, considering different types of tubular spaces. In all cases, the solution provided by the proposed algorithm is smoother, shorter, and has a higher accuracy with a faster calculation speed than that obtained by Dijkstra’s method. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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21 pages, 2222 KiB  
Article
Adaptive and Lightweight Abnormal Node Detection via Biological Immune Game in Mobile Multimedia Networks
by Yajing Zhang, Kai Wang and Jinghui Zhang
Algorithms 2021, 14(12), 368; https://doi.org/10.3390/a14120368 - 20 Dec 2021
Cited by 2 | Viewed by 2428
Abstract
Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection [...] Read more.
Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection overhead. The algorithm can adapt to the highly dynamic mobile multimedia networking environment with a large number of heterogeneous nodes and multi-source big data. Specifically, the heterogeneous problem of nodes is solved based on the non-specificity of an immune algorithm. A niche strategy is used to identify dangerous areas, and antibody division generates an antibody library that can be updated online, so as to realize the dynamic detection of the abnormal behavior of nodes. Moreover, the priority of node recovery for abnormal nodes is decided through a game between nodes without causing excessive resource consumption for security detection. The results of comparative experiments show that the proposed algorithm has a relatively high detection rate and a low false-positive rate, can effectively reduce consumption time, and has good level of adaptability under the condition of dynamic nodes. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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27 pages, 9325 KiB  
Article
DMFO-CD: A Discrete Moth-Flame Optimization Algorithm for Community Detection
by Mohammad H. Nadimi-Shahraki, Ebrahim Moeini, Shokooh Taghian and Seyedali Mirjalili
Algorithms 2021, 14(11), 314; https://doi.org/10.3390/a14110314 - 28 Oct 2021
Cited by 35 | Viewed by 4614
Abstract
In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In [...] Read more.
In this paper, a discrete moth–flame optimization algorithm for community detection (DMFO-CD) is proposed. The representation of solution vectors, initialization, and movement strategy of the continuous moth–flame optimization are purposely adapted in DMFO-CD such that it can solve the discrete community detection. In this adaptation, locus-based adjacency representation is used to represent the position of moths and flames, and the initialization process is performed by considering the community structure and the relation between nodes without the need of any knowledge about the number of communities. Solution vectors are updated by the adapted movement strategy using a single-point crossover to distance imitating, a two-point crossover to calculate the movement, and a single-point neighbor-based mutation that can enhance the exploration and balance exploration and exploitation. The fitness function is also defined based on modularity. The performance of DMFO-CD was evaluated on eleven real-world networks, and the obtained results were compared with five well-known algorithms in community detection, including GA-Net, DPSO-PDM, GACD, EGACD, and DECS in terms of modularity, NMI, and the number of detected communities. Additionally, the obtained results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. In the comparison with other comparative algorithms, the results show that the proposed DMFO-CD is competitive to detect the correct number of communities with high modularity. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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12 pages, 11699 KiB  
Article
Optimal Transport in Multilayer Networks for Traffic Flow Optimization
by Abdullahi Adinoyi Ibrahim, Alessandro Lonardi and Caterina De Bacco
Algorithms 2021, 14(7), 189; https://doi.org/10.3390/a14070189 - 23 Jun 2021
Cited by 21 | Viewed by 4268
Abstract
Modeling traffic distribution and extracting optimal flows in multilayer networks is of the utmost importance to design efficient, multi-modal network infrastructures. Recent results based on optimal transport theory provide powerful and computationally efficient methods to address this problem, but they are mainly focused [...] Read more.
Modeling traffic distribution and extracting optimal flows in multilayer networks is of the utmost importance to design efficient, multi-modal network infrastructures. Recent results based on optimal transport theory provide powerful and computationally efficient methods to address this problem, but they are mainly focused on modeling single-layer networks. Here, we adapt these results to study how optimal flows distribute on multilayer networks. We propose a model where optimal flows on different layers contribute differently to the total cost to be minimized. This is done by means of a parameter that varies with layers, which allows to flexibly tune the sensitivity to the traffic congestion of the various layers. As an application, we consider transportation networks, where each layer is associated to a different transportation system, and show how the traffic distribution varies as we tune this parameter across layers. We show an example of this result on the real, 2-layer network of the city of Bordeaux with a bus and tram, where we find that in certain regimes, the presence of the tram network significantly unburdens the traffic on the road network. Our model paves the way for further analysis of optimal flows and navigability strategies in real, multilayer networks. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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20 pages, 3379 KiB  
Article
Community Structure and Systemic Risk of Bank Correlation Networks Based on the U.S. Financial Crisis in 2008
by Yajing Huang and Feng Chen
Algorithms 2021, 14(6), 162; https://doi.org/10.3390/a14060162 - 22 May 2021
Cited by 5 | Viewed by 2666
Abstract
This paper studies the community structure of the bank correlation network in the financial system and analyzes the systemic risk of the community sub-networks. Based on the balance sheet data of U.S. commercial banks from 2008, we establish a bank correlation network for [...] Read more.
This paper studies the community structure of the bank correlation network in the financial system and analyzes the systemic risk of the community sub-networks. Based on the balance sheet data of U.S. commercial banks from 2008, we establish a bank correlation network for each state according to the banks’ investment portfolio ratio. First, we analyze the community structure of each bank’s correlation network and verify the effectiveness of the community division from the point of view of the importance of nodes. Then, combining the data of failed banks after the 2008 financial crisis, we find that for small communities, the financial systemic risk will appear to have obvious volatility, and it is quite likely to reach an extremely high level. With the increase in the number of nodes in the community, systemic risk will tend towards a stable and low level. Furthermore, if only communities with failed banks are considered, the regression analysis shows that systemic risk and the size of the community almost follow a power law distribution trend. These results reveal the importance of supervising the banking system at the level of community sub-networks, which has certain guiding significance for the stability of the financial system. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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13 pages, 424 KiB  
Article
A Set-Theoretic Approach to Modeling Network Structure
by John L. Pfaltz
Algorithms 2021, 14(5), 153; https://doi.org/10.3390/a14050153 - 11 May 2021
Cited by 1 | Viewed by 2598
Abstract
Three computer algorithms are presented. One reduces a network N to its interior, I. Another counts all the triangles in a network, and the last randomly generates networks similar to N given just its interior I. However, these algorithms are not [...] Read more.
Three computer algorithms are presented. One reduces a network N to its interior, I. Another counts all the triangles in a network, and the last randomly generates networks similar to N given just its interior I. However, these algorithms are not the usual numeric programs that manipulate a matrix representation of the network; they are set-based. Union and meet are essential binary operators; contained_in is the basic relational comparator. The interior I is shown to have desirable formal properties and to provide an effective way of revealing “communities” in social networks. A series of networks randomly generated from I is compared with the original network, N. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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17 pages, 3167 KiB  
Article
Optimization of the Multi-Facility Location Problem Using Widely Available Office Software
by Petr Němec and Petr Stodola
Algorithms 2021, 14(4), 106; https://doi.org/10.3390/a14040106 - 26 Mar 2021
Cited by 8 | Viewed by 4932
Abstract
Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle [...] Read more.
Multi-facility location problem is a type of task often solved (not only) in logistics. It consists in finding the optimal location of the required number of centers for a given number of points. One of the possible solutions is to use the principle of the genetic algorithm. The Solver add-in, which uses the evolutionary method, is available in the Excel office software. It was used to solve the benchmark in 4 levels of difficulty (from 5 centers for 25 points to 20 centers for 100 points), and one task from practice. The obtained results were compared with the results obtained by the metaheuristic simulated annealing method. It was found that the results obtained by the evolutionary method are sufficiently accurate. Their accuracy depends on the complexity of the task and the performance of the HW used. The advantage of the proposed solution is easy availability and minimal requirements for user knowledge. Full article
(This article belongs to the Special Issue Network Science: Algorithms and Applications)
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