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Journal articles on the topic 'Network measures'

Author: Grafiati

Published: 9 March 2023

Last updated: 27 July 2025

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1

Xu, Zhen-Peng, Julio I. de Vicente, Liang-Liang Sun, and Sixia Yu. "Quantum network-entanglement measures." Quantum 9 (May 6, 2025): 1736. https://doi.org/10.22331/q-2025-05-06-1736.

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Quantum networks are of high interest nowadays and a quantum internet has been long envisioned. Network-entanglement adapts the notion of entanglement to the network scenario and network-entangled states are considered to be a resource to overcome the limitations of a given network structure. In this work, we introduce measures of quantum network-entanglement that are well-defined within the general framework of quantum resource theories, which at the same time have a clear operational interpretation characterizing the extra resources necessary to prepare a targeted quantum state within a give

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Kincaid, Rex K., and David J. Phillips. "Network topology measures." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 6 (2011): 557–65. http://dx.doi.org/10.1002/wics.180.

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Zhang, Wen Jie. "Network Security Vulnerabilities and Preventive Measures." Applied Mechanics and Materials 433-435 (October 2013): 1674–78. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1674.

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As the arrival times network economy and social networks, computers and computer networks popularity indicates that a ubiquitous network will go into not the state. As the use of network and the virus has become increasingly serious, virus writers from the original to show off to obtain invalid income, the resulting harm is incalculable. This situation not only jeopardize the current development of the Internet but also undermined the reality of social stability and unity, therefore, this paper is on how the network vulnerabilities, how to survive and how to protection, so that there are looph

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Aytaç, Aysun, and Tufan Turaci. "Vulnerability Measures of Transformation Graph Gxy+." International Journal of Foundations of Computer Science 26, no. 06 (2015): 667–75. http://dx.doi.org/10.1142/s0129054115500379.

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Several factors have to be taken into account in the design of large interconnection networks. Optimal design is important both to achieve good performance and to reduce the cost of construction and maintenance. Practical communication networks are exposed to failures of network components. Failures between nodes and connections happen and it is desirable that a network is robust in the sense that a limited number of failures does not break down the whole system. Robustness of the network topology is a key aspect in the design of computer networks. A variety of measures have been proposed in t

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Boehmke, Frederick J., Olga Chyzh, and Cameron G. Thies. "Addressing Endogeneity in Actor-Specific Network Measures." Political Science Research and Methods 4, no. 1 (2015): 123–49. http://dx.doi.org/10.1017/psrm.2015.34.

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The study of international relations (IR), and political science more broadly, has derived great benefits from the recent growth of conceptualizing and modeling political phenomena within their broader network contexts. More than just a novel approach to evaluating old puzzles, network analysis provides a whole new way of theoretical thinking. Challenging the traditional dyad-driven approach to the study of IR, networks highlight actor interdependence that goes beyond dyads and emphasizes that many traditional IR variables, such as conflict, trade, alliances, or international organization memb

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Lordan, Oriol, and Jose M. Sallan. "Dynamic measures for transportation networks." PLOS ONE 15, no. 12 (2020): e0242875. http://dx.doi.org/10.1371/journal.pone.0242875.

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Most complex network analyses of transportation systems use simplified static representations obtained from existing connections in a time horizon. In static representations, travel times, waiting times and compatibility of schedules are neglected, thus losing relevant information. To obtain a more accurate description of transportation networks, we use a dynamic representation that considers synced paths and that includes waiting times to compute shortest paths. We use the shortest paths to define dynamic network, node and edge measures to analyse the topology of transportation networks, comp

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Bibi, Fizza, Hikmat Khan, Tassawar Iqbal, Muhammad Farooq, Irfan Mehmood, and Yunyoung Nam. "Ranking Authors in an Academic Network Using Social Network Measures." Applied Sciences 8, no. 10 (2018): 1824. http://dx.doi.org/10.3390/app8101824.

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Online social networks are widely used platforms that enable people to connect with each other. These social media channels provide an active communication platform for people, and they have opened new venues of research for the academic world and business. One of these research areas is measuring the influential users in online social networks; and the same is true for academic networks where finding influential authors is an area of interest. In an academic network, citation count, h-index and their variations are used to find top authors. In this article, we propose the adoption of establis

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Kansky, Karl, and Pascal Danscoine. "Measures of network structure." Flux 5, no. 1 (1989): 89–121. http://dx.doi.org/10.3406/flux.1989.913.

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Banisch, Ralf, Péter Koltai, and Kathrin Padberg-Gehle. "Network measures of mixing." Chaos: An Interdisciplinary Journal of Nonlinear Science 29, no. 6 (2019): 063125. http://dx.doi.org/10.1063/1.5087632.

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10

Li, Yan. "Network Security Protection Measures." Advanced Materials Research 971-973 (June 2014): 1659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1659.

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Safety is the premise of network operation, network security is not only a single point of security, but the safety of the information network, need to be done from various solid protection. Analyzed the current form and face all kinds of LAN network security threats and network security precautions, technology, this paper expounds the LAN the importance of the current network security in our lives.

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Vardi, Yehuda, and Cun-Hui Zhang. "Measures of Network Vulnerability." IEEE Signal Processing Letters 14, no. 5 (2007): 313–16. http://dx.doi.org/10.1109/lsp.2006.888290.

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Ali, Ali Saber, and Kaylan Hamid Noor. "Complex networks analysis: centrality measures." Complex networks analysis: centrality measures 29, no. 3 (2023): 1642–47. https://doi.org/10.11591/ijeecs.v29.i3.pp1642-1647.

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The centrality of an edge in a graph is proposed to be the degree of sensitivity of a graph distance function to the weight of the edge under consideration. Many centrality metrics are available in network analysis and are effectively used in the investigation of social network properties. Node position is one of them. In this paper, we propose a novel importance of nodes showing how to locate the most essential nodes in a network and to construct a centrality measure for each node in the network, sort the nodes by centralities, and focus on the top ranked nodes, which are the most relevant in

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JORGENSEN, THOMAS D., BARRY P. HAYNES, and CHARLOTTE C. F. NORLUND. "PRUNING ARTIFICIAL NEURAL NETWORKS USING NEURAL COMPLEXITY MEASURES." International Journal of Neural Systems 18, no. 05 (2008): 389–403. http://dx.doi.org/10.1142/s012906570800166x.

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This paper describes a new method for pruning artificial neural networks, using a measure of the neural complexity of the neural network. This measure is used to determine the connections that should be pruned. The measure computes the information-theoretic complexity of a neural network, which is similar to, yet different from previous research on pruning. The method proposed here shows how overly large and complex networks can be reduced in size, whilst retaining learnt behaviour and fitness. The technique proposed here helps to discover a network topology that matches the complexity of the

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Saber, Ali Ali, and Noor Kaylan Hamid. "Complex networks analysis: centrality measures." Indonesian Journal of Electrical Engineering and Computer Science 29, no. 3 (2023): 1642. http://dx.doi.org/10.11591/ijeecs.v29.i3.pp1642-1647.

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<span lang="EN-US">The centrality of an edge in a graph is proposed to be the degree of sensitivity of a graph distance function to the weight of the edge under consideration. Many centrality metrics are available in network analysis and are effectively used in the investigation of social network properties. Node position is one of them. In this paper, we propose a novel importance of nodes showing how to locate the most essential nodes in a network and to construct a centrality measure for each node in the network, sort the nodes by centralities, and focus on the top ranked nodes, which

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Zarezadeh, S., M. Asadi, and S. Eftekhar. "SIGNATURE-BASED INFORMATION MEASURES OF MULTI-STATE NETWORKS." Probability in the Engineering and Informational Sciences 33, no. 3 (2018): 438–59. http://dx.doi.org/10.1017/s0269964818000177.

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The signature matrix of an n-component three-state network (system), which depends only on the network structure, is a useful tool for comparing the reliability and stochastic properties of networks. In this paper, we consider a three-state network with states up, partial performance, and down. We assume that the network remains in state up, for a random time T1 and then moves to state partial performance until it fails at time T>T1. The signature-based expressions for the conditional entropy of T given T1, the joint entropy, Kullback-Leibler (K-L) information, and mutual information of the

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Rheinwalt, Aljoscha, Norbert Marwan, Jürgen Kurths, Peter Werner, and Friedrich-Wilhelm Gerstengarbe. "Boundary effects in network measures of spatially embedded networks." EPL (Europhysics Letters) 100, no. 2 (2012): 28002. http://dx.doi.org/10.1209/0295-5075/100/28002.

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Vörös, András, and Tom A. B. Snijders. "Cluster analysis of multiplex networks: Defining composite network measures." Social Networks 49 (May 2017): 93–112. http://dx.doi.org/10.1016/j.socnet.2017.01.002.

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Zhao, Yiji, Youfang Lin, Zhihao Wu, Yang Wang, and Haomin Wen. "Context-aware Distance Measures for Dynamic Networks." ACM Transactions on the Web 16, no. 1 (2022): 1–34. http://dx.doi.org/10.1145/3476228.

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Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the con

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Batool, Zahra, Muhammad Junaid, Muhammad Naeem, et al. "Identifying Disease Comorbidity Patterns Using Centrality Measures in Computing." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 2964–75. http://dx.doi.org/10.47059/revistageintec.v11i4.2332.

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Social network analysis has been increasingly employed to study patterns in diverse areas of disciplines such as crowd management, air passenger and freight transportation, business modelling and analysis, online social movements and bioinformatics. Over the years, human disease networks have been studied to analyze Human Disease, Genotype, and Phenotype networks. This study explores human Disease Network based on their symptoms by employing different social network analysis such as centrality measures of network, community detection, overlapping communities. We studied relationships of sympto

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Gupta, Shilpa, and G. L. Pahuja. "A New SEN Minus: Design and Reliability Measures." International Journal of Reliability, Quality and Safety Engineering 23, no. 04 (2016): 1650012. http://dx.doi.org/10.1142/s0218539316500121.

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Multistage interconnection networks (MIN) are becoming attractive choice as they provide fast and efficient communication at reasonable cost, for multiprocessing systems. Shuffle exchange network (SEN) are specific class of MIN characterized as lowest cost unipath MIN. Several developments have made SEN MIN fault tolerant with redundant paths by increasing the number or size of switching elements (SE). However, recently [Formula: see text] has been advanced by reducing the number of stages, but has serious limitation namely: (i) partial connectivity of each source–destination pair, (ii) unique

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Martin, Christoph, and Peter Niemeyer. "Influence of measurement errors on networks: Estimating the robustness of centrality measures." Network Science 7, no. 2 (2019): 180–95. http://dx.doi.org/10.1017/nws.2019.12.

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AbstractMost network studies rely on a measured network that differs from the underlying network which is obfuscated by measurement errors. It is well known that such errors can have a severe impact on the reliability of network metrics, especially on centrality measures: a more central node in the observed network might be less central in the underlying network. Previous studies have dealt either with the general effects of measurement errors on centrality measures or with the treatment of erroneous network data. In this paper, we propose a method for estimating the impact of measurement erro

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CHANG, K. H., B. C. CHOI, SEONG-MIN YOON, and KYUNGSIK KIM. "MULTIFRACTAL MEASURES ON SMALL-WORLD NETWORKS." Fractals 14, no. 02 (2006): 119–23. http://dx.doi.org/10.1142/s0218348x0600312x.

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We investigate the multifractals of the first passage time on a one-dimensional small-world network with reflecting and absorbing barriers. The multifractals can be obtained from the distribution of the first passage time at which the random walker arrives for the first time at an absorbing barrier after starting from an arbitrary initial site. Our simulation is found to estimate the fractal dimension D0 = 0.920 ~ 0.930 for the different network sizes and random rewiring fractions. In particular, the multifractal structure breaks down into a small-world network, when the rewiring fraction p is

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Ho, Thi Kim Thoa. "Effect of similarity measures in diffusion prediction on homogeneous and heterogeneous bibliographic network." Hue University Journal of Science: Techniques and Technology 132, no. 2B (2023): 17–31. http://dx.doi.org/10.26459/hueunijtt.v132i2b.7348.

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This paper evaluates the effect of similarity measures in predicting diffusion on homogeneous and heterogeneous bibliographic networks. The bibliographic network is analyzed within a homogeneous network and heterogeneous network, where a co-author relationship exists for the former, and multiple types of meta paths are considered for the latter. The supervised learning method is used to predict whether a node will be active with a topic or not. The features are extracted as the activation probability of a node, which represents the maximum of the activation probabilities of the neighbors of th

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ISOZAKI, Masanori, and Takayuki KOBAYASHI. "Environmental Measures in Power Network." Journal of The Institute of Electrical Engineers of Japan 126, no. 4 (2006): 210–13. http://dx.doi.org/10.1541/ieejjournal.126.210.

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Dehmer, Matthias, Zengqiang Chen, Yongtang Shi, et al. "On efficient network similarity measures." Applied Mathematics and Computation 362 (December 2019): 124521. http://dx.doi.org/10.1016/j.amc.2019.06.035.

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BELL, JOCELYN R. "Subgroup centrality measures." Network Science 2, no. 2 (2014): 277–97. http://dx.doi.org/10.1017/nws.2014.15.

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AbstractIn this paper we examine natural generalizations of four widely used centrality measures to subgroups of nodes in a network. This allows for a division into local and global influence. As an example, we analyze a classic network and discuss previously hidden features made visible by these new techniques. Network-wide measures and centralization formulae are derived.

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S Kale, Rakesh. "The Vulnerabilities, Threats and Counter measures in Wireless Network Security." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 1218–20. http://dx.doi.org/10.21275/mr23419121210.

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Roza, Dastres, and Soori Mohsen. "A Review in Recent Development of Network Threats and Security Measures." International Journal of Computer and Information Engineering 15, no. 1 (2021): 75–81. https://doi.org/10.5281/zenodo.4498763.

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Networks are vulnerable devices due to their basic feature of facilitating remote access and data communication. The information in the networks needs to be kept secured and safe in order to provide an effective communication and sharing device in the web of data. Due to challenges and threats of the data in networks, the network security is one of the most important considerations in information technology infrastructures. As a result, the security measures are considered in the network in order to decrease the probability of accessing the secured data by the hackers. The purpose of network s

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Aynulin, Rinat, and Pavel Chebotarev. "Extending Proximity Measures to Attributed Networks for Community Detection." Complex Systems 30, no. 4 (2021): 441–55. http://dx.doi.org/10.25088/complexsystems.30.4.441.

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Proximity measures on graphs are extensively used for solving various problems in network analysis, including community detection. Previous studies have considered proximity measures mainly for networks without attributes. However, attribute information, node attributes in particular, allows a more in-depth exploration of the network structure. This paper extends the definition of a number of proximity measures to the case of attributed networks. To take node attributes into account, attribute similarity is embedded into the adjacency matrix. Obtained attribute-aware proximity measures are num

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Siami, Milad, Sadegh Bolouki, Bassam Bamieh, and Nader Motee. "Centrality Measures in Linear Consensus Networks With Structured Network Uncertainties." IEEE Transactions on Control of Network Systems 5, no. 3 (2018): 924–34. http://dx.doi.org/10.1109/tcns.2017.2655731.

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Seema, Varghese. "A Survey on Centrality Measures of a Network." A Survey on Centrality Measures of a Network 8, no. 10 (2023): 4. https://doi.org/10.5281/zenodo.10002661.

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A node's centrality score is an index that identifies its importance inside a network. Depending on the goal of the study, different writers define different centrality measures. Numerous fields, including sociology, neurobiology, communication networks, electrical networks, etc., find use for these measurements. The goal of this work is to provide a thorough overview of degree, closeness, eigen vector, and betweenness centrality.Keywords:- Centrality Measure; Degree Centrality; Closeness Centrality; Eigen Vector Centrality; Betweenness Centrality.

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Nwabueze, C. A., and R. E. Usiade. "Security Vulnerability and Counter Measures in Mobile Ad Hoc Networks." International Journal of Trend in Scientific Research and Development 4, no. 2 (2020): 721–26. https://doi.org/10.5281/zenodo.3843000.

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The rising concern for beneficial, easy design and deployable, flexible, reliable, cost effective and scalable wireless network has led to the evolution of mobile Ad hoc networks MANETs . This type of network in its own peculiarity is a wireless, mobile, infrastructure less network technology. It has been found to be very useful in military, commercial, personal, emergency related applications but not without some security challenges. The security of MANET is an important challenge to network engineers due to its unmonitored deployment nature and inherent resource limitation. This paper presen

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Zhao, Shuying, and Shaowei Sun. "A study on centrality measures in weighted networks: A case of the aviation network." AIMS Mathematics 9, no. 2 (2024): 3630–45. http://dx.doi.org/10.3934/math.2024178.

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<abstract><p>Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. An aviation network is a typical complex network, and accurately identifying the key city nodes in the aviation network can help us better prevent network attacks and control the spread of diseases. In this paper, a method for identifying key nodes in undirected weighted networks, called weighted Laplacian energy centrality, was proposed and applied to an aviation network constructed from real flight data. Based on the analysis of

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Eremeev, Igor, Maxim Tatarka, Fedor Shuvaev, and Andrey Tsyganov. "Comparative Analysis of Centrality Measures of Network Nodes based on Principal Component Analysis." Informatics and Automation 19, no. 6 (2020): 1307–31. http://dx.doi.org/10.15622/ia.2020.19.6.7.

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. The analysis of networks of a diverse nature, which are citation networks, social networks or information and communication networks, includes the study of topological properties that allow one to assess the relationships between network nodes and evaluate various characteristics, such as the density and diameter of the network, related subgroups of nodes, etc. For this, the network is represented as a graph – a set of vertices and edges between them. One of the most important tasks of network analysis is to estimate the significance of a node (or in terms of graph theory – a vertex). For th

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Wang, Bing, Zhiwen Sun, and Yuexing Han. "A Path-Based Distribution Measure for Network Comparison." Entropy 22, no. 11 (2020): 1287. http://dx.doi.org/10.3390/e22111287.

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As network data increases, it is more common than ever for researchers to analyze a set of networks rather than a single network and measure the difference between networks by developing a number of network comparison methods. Network comparison is able to quantify dissimilarity between networks by comparing the structural topological difference of networks. Here, we propose a kind of measures for network comparison based on the shortest path distribution combined with node centrality, capturing the global topological difference with local features. Based on the characterized path distribution

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Tanzi, Matteo, and Lai-Sang Young. "Existence of physical measures in some excitation–inhibition networks*." Nonlinearity 35, no. 2 (2021): 889–915. http://dx.doi.org/10.1088/1361-6544/ac3eb6.

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Abstract In this paper we present a rigorous analysis of a class of coupled dynamical systems in which two distinct types of components, one excitatory and the other inhibitory, interact with one another. These network models are finite in size but can be arbitrarily large. They are inspired by real biological networks, and possess features that are idealizations of those in biological systems. Individual components of the network are represented by simple, much studied dynamical systems. Complex dynamical patterns on the network level emerge as a result of the coupling among its constituent s

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Fletcher, Jack McKay, and Thomas Wennekers. "From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity." International Journal of Neural Systems 28, no. 02 (2018): 1750013. http://dx.doi.org/10.1142/s0129065717500137.

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It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate

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G.Geetharamani, G. Geetharamani, B. Palpandi B.Palpandi, and J. Arun Pandian. "System Performance Measures Analysis for Heterogeneous Computing Network using Fuzzy Queue." Paripex - Indian Journal Of Research 3, no. 5 (2012): 153–59. http://dx.doi.org/10.15373/22501991/may2014/51.

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Laghridat, C., and M. Essalih. "Generalization of certain centrality measures for social networks analysis." Mathematical Modeling and Computing 12, no. 2 (2025): 621–27. https://doi.org/10.23939/mmc2025.02.621.

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No doubt, the majority of people have personal accounts on several social networks, which allow them to exchange information with their family and friends and to follow their interests. A social network can be defined by a set of social entities and the relationships that these social entities have with each other directly, or indirectly through the length of the links between them. For a better understanding the interest of the social communication between people, which is the main key for the process of social network analysis, we will use indicators that are supposed to determine the notion

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Lee, Jae-Yun. "Centrality Measures for Bibliometric Network Analysis." Journal of the Korean Society for Library and Information Science 40, no. 3 (2006): 191–214. http://dx.doi.org/10.4275/kslis.2006.40.3.191.

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Woldeyohannes, Yordanos T., and Yuming Jiang. "Measures for Network Structural Dependency Analysis." IEEE Communications Letters 22, no. 10 (2018): 2052–55. http://dx.doi.org/10.1109/lcomm.2018.2864109.

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Sobrinho, Fuad Gattaz, Cristiane Chaves Gattaz, and Oscar Ivan Palma Pacheco. "COMPLEXITY MEASURES FOR NETWORK PROCESS EVOLUTION." Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Process Science 15, no. 4 (2011): 95–115. http://dx.doi.org/10.3233/jid-2011-15406.

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The foundation for network process evolution research is the modeling of network structure and behavior complexity. With such a model, network systems can be directed toward acquiring good maintainability attributes according to the principles of engineering. In this paper, a Process Management Network (PMN) model is developed to acquire directly from the target process codes the knowledge hidden among and within components of network systems. With the knowledge acquired by the PMN model, network structure and behavior complexity measures in terms of partitioning, restructuring and rewriting c

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Barker, Kash, Jose Emmanuel Ramirez-Marquez, and Claudio M. Rocco. "Resilience-based network component importance measures." Reliability Engineering & System Safety 117 (September 2013): 89–97. http://dx.doi.org/10.1016/j.ress.2013.03.012.

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Kleiner, Kurt. "Nanowire network measures cells' electrical signals." New Scientist 202, no. 2705 (2009): 20. http://dx.doi.org/10.1016/s0262-4079(09)61110-6.

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Hamasuna, Yukihiro, Daiki Kobayashi, Ryo Ozaki, and Yasunori Endo. "Cluster Validity Measures for Network Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 4 (2018): 544–50. http://dx.doi.org/10.20965/jaciii.2018.p0544.

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Modularity is one of the evaluation measures for network partitions and is used as the merging criterion in the Louvain method. To construct useful cluster validity measures and clustering methods for network data, network cluster validity measures are proposed based on the traditional indices. The effectiveness of the proposed measures are compared and applied to determine the optimal number of clusters. The network cluster partitions of various network data which are generated from the Polaris dataset are obtained byk-medoids with Dijkstra’s algorithm and evaluated by the proposed measures a

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Grando, Felipe, Lisandro Z. Granville, and Luis C. Lamb. "Machine Learning in Network Centrality Measures." ACM Computing Surveys 51, no. 5 (2019): 1–32. http://dx.doi.org/10.1145/3237192.

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Sukumar, Nagamani, Michael P. Krein, Ganesh Prabhu, Sudeepto Bhattacharya, and Subhabrata Sen. "Network Measures for Chemical Library Design." Drug Development Research 75, no. 6 (2014): 402–11. http://dx.doi.org/10.1002/ddr.21218.

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Baroud, Hiba, Kash Barker, and Jose Ramirez-Marquez. "Measures of Inland Waterway Network Resilience." INCOSE International Symposium 23, no. 1 (2013): 1354–67. http://dx.doi.org/10.1002/j.2334-5837.2013.tb03092.x.

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Jing, Weiwei, Xiangdong Xu, and Yichao Pu. "Route Redundancy-Based Network Topology Measure of Metro Networks." Journal of Advanced Transportation 2019 (July 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/4576961.

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The metro system plays a very important role in the urban multimodal transportation system, yet it is susceptible to accidents. A well-designed metro system needs to provide alternative routes to travellers both in the disruptive events and the normal operating conditions for providing rerouting opportunities and balancing crowded lines. This paper provides a new dimension of assessing metro network performance—travellers’ route redundancy (or route diversity), which is defined as the number of behaviourally effective routes between each origin-destination (O-D) pair in the network. The route

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Bertaccini, Daniele, and Alessandro Filippo. "A proposal for ranking through selective computation of centrality measures." PLOS ONE 18, no. 9 (2023): e0289488. http://dx.doi.org/10.1371/journal.pone.0289488.

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In complex network analysis it is essential to investigate the alteration of network structures that results from the targeted removal of vertices or edges, ranked by centrality measures. Unfortunately, a sequential recalculation of centralities after each node elimination is often impractical for large networks, and computing rankings only at the beginning often does not accurately reflect the actual scenario. Here we propose a first result on the computational complexity of the sequential approach when nodes are removed from a network according to some centrality measures based on matrix fun

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