Journal articles: 'Network measures'

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Kincaid, Rex K., and David J. Phillips. "Network topology measures." Wiley Interdisciplinary Reviews: Computational Statistics 3, no. 6 (June 14, 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 loopholes in our network deeper understanding of network vulnerability and protection so that we have a new breakthrough for the network to provide a good environment for development.

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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 (September 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 the literature to quantify the robustness of networks and a number of graph-theoretic parameters have been used to derive formulas for calculating network reliability. In this paper, we study the vulnerability of interconnection networks to the failure of individual nodes, using a graph-theoretic concept of domination and strong-weak domination numbers of the transformation graph Gxy+ as a measure of network robustness.

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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 (August 24, 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 memberships must be treated and studied as networks. Properties of these networks (e.g., polarization, density), and of actor positions within them (e.g., similarity, centrality), will then reveal important insights about international events. Network analysis, however, is not yet fully adapted to account for important methodological issues common to IR research, specifically the issue of endogeneity or possible nonindependence between actors’ position within international networks and the outcomes of interest: for example, alliance network may be nonindependent from the conflict or trade network. We adopt an instrumental variable approach to explore and address the issue of endogeneity in network context. We illustrate the issue and the advantages of our approach with Monte Carlo analysis, as well as with several empirical examples from IR literature.

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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 (June 2019): 063125. http://dx.doi.org/10.1063/1.5087632.

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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 (May 2007): 313–16. http://dx.doi.org/10.1109/lsp.2006.888290.

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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 (October 4, 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 established social network measures, including centrality and prestige, in an academic network to compute the rank of authors. For the empirical analysis, the widely-used dataset of the Digital Bibliography and Library Project (DBLP) is exploited in this research, and the micro-level properties of the network formed in the DBLP co-authorship network are studied. Afterwards, the results are computed using social network measures and evaluated using the standard ranking performance evaluation measures, including Kendall correlation, Overlapping Similarlity (OSim) and Spearman rank-order correlation. The results reveal that the centrality measures are significantly correlated with the citation count and h-index. Consequently, social network measures have potential to be used in an academic network to rank the authors.

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Lordan, Oriol, and Jose M. Sallan. "Dynamic measures for transportation networks." PLOS ONE 15, no. 12 (December 3, 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, comparable with measures obtained from static representations. We illustrate the application of these measures with a toy model and a real transportation network built from schedules of a low-cost carrier. Results show remarkable differences between measures of static and dynamic representations, demonstrating the limitations of the static representation to obtain accurate information of transportation networks.

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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 (October 1, 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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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 (October 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 problem it is meant to solve. This novel pruning technique is tested in a robot control domain, simulating a racecar. It is shown, that the proposed pruning method is a significant improvement over the most commonly used pruning method Magnitude Based Pruning. Furthermore, some of the pruned networks prove to be faster learners than the benchmark network that they originate from. This means that this pruning method can also help to unleash hidden potential in a network, because the learning time decreases substantially for a pruned a network, due to the reduction of dimensionality of the network.

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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 (March 1, 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 are the most relevant in terms of this centrality measure. Our research aims to explain how to identify the most important nodes in networks. A centrality metric should be established for each node in the network, and then the nodes based on their centralities, focusing on the top-ranked nodes, which in light of this importance, might be regarded as the most pertinent measure.</span>

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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 (June 14, 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 lifetimes T and T1 are presented. It is shown that the K-L information, and mutual information between T1 and T depend only on the network structure (i.e., depend only to the signature matrix of the network). Some signature-based stochastic comparisons are also made to compare the K-L of the state lifetimes in two different three-state networks. Upper and lower bounds for the K-L divergence and mutual information between T1 and T are investigated. Finally the results are extended to n-component multi-state networks. Several examples are examined graphically and numerically.

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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, Yusen Zhang, Shailesh Tripathi, Modjtaba Ghorbani, Abbe Mowshowitz, and Frank Emmert-Streib. "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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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 (February 28, 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 construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

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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 (August 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 path. A new method has been proposed in this paper to develop a new topology of MIN with one stage less than the basic unipath MIN of same class with multiple and disjoint path facility that mitigates the shortcomings of the above network and is truly [Formula: see text] MIN. Due to less number of stages used in the proposed network communication delays are also reduced as the path length is reduced. Parametric performances such as Terminal, Broadcast and Network Reliabilities, MTTF, Bandwidth have been computed for different network sizes and demonstrated that it not only outperforms other SEN variants, but has improved features of fault tolerance all because of disjoint minimal path set. Further the comments generated previously in literature about better reliability performance of [Formula: see text] than other two networks [Formula: see text] have been refuted and have demonstrated that [Formula: see text]2 network has better performance than other two for larger network size. Also it can be concluded that the performance of proposed [Formula: see text] is best among all these networks.

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Batool, Zahra, Muhammad Junaid, Muhammad Naeem, Mehmood Ahmed, Luqman Shah, Yousaf Saeed, Ali Imran Jehangiri, and Fahad Ali Khan. "Identifying Disease Comorbidity Patterns Using Centrality Measures in Computing." Revista Gestão Inovação e Tecnologias 11, no. 4 (July 22, 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 symptoms with diseases on meso-level in order to detect comorbidity pattern of communities in disease network. This help us to understand the underlying patterns of diseases based on symptoms and find out that how different disease communities are correlated by detecting overlapping communities.

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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 (June 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 larger than the critical value pc = 0.3. Our simulation results are compared with the numerical computations for regular networks.

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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 (September 2018): 924–34. http://dx.doi.org/10.1109/tcns.2017.2655731.

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BELL, JOCELYN R. "Subgroup centrality measures." Network Science 2, no. 2 (August 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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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 (January 15, 2012): 153–59. http://dx.doi.org/10.15373/22501991/may2014/51.

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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 (June 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 errors on the reliability of a centrality measure, given the measured network and assumptions about the type and intensity of the measurement error. This method allows researchers to estimate the robustness of a centrality measure in a specific network and can, therefore, be used as a basis for decision-making. In our experiments, we apply this method to random graphs and real-world networks. We observe that our estimation is, in the vast majority of cases, a good approximation for the robustness of centrality measures. Beyond this, we propose a heuristic to decide whether the estimation procedure should be used. We analyze, for certain networks, why the eigenvector centrality is less robust than, among others, the pagerank. Finally, we give recommendations on how our findings can be applied to future network studies.

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Gil, Jorge. "Street network analysis “edge effects”: Examining the sensitivity of centrality measures to boundary conditions." Environment and Planning B: Urban Analytics and City Science 44, no. 5 (May 20, 2016): 819–36. http://dx.doi.org/10.1177/0265813516650678.

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With increased interest in the use of network analysis to study the urban and regional environment, it is important to understand the sensitivity of centrality analysis results to the so-called “edge effect”. Most street network models have artificial boundaries, and there are principles that can be applied to minimise or eliminate the effect of the boundary condition. However, the extent of this impact has not been systematically studied and remains little understood. In this article we present an empirical study on the impact of different network model boundaries on the results of closeness and betweenness centrality analysis of street networks. The results demonstrate that the centrality measures are affected differently by the edge effect, and that the same centrality measure is affected differently depending on the type of network distance used. These results highlight the importance, in any study of street networks, of defining the network's boundary in a way that is relevant to the research question, and of selecting appropriate analysis parameters and statistics.

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Aynulin, Rinat, and Pavel Chebotarev. "Extending Proximity Measures to Attributed Networks for Community Detection." Complex Systems 30, no. 4 (December 15, 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 numerically studied in the context of community detection in real-world networks.

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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 (September 1, 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 (October 2018): 2052–55. http://dx.doi.org/10.1109/lcomm.2018.2864109.

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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 (April 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 (July 20, 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 as well as the modularity. Our numerical experiments show that the Dunn’s index and the Xie-Beni’s index-based measures are effective for network partitions compared to other indices.

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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 (January 23, 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 (September 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 (June 2013): 1354–67. http://dx.doi.org/10.1002/j.2334-5837.2013.tb03092.x.

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Altay, Gökmen, Zeyneb Kurt, Matthias Dehmer, and Frank Emmert-Streib. "Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures." Evolutionary Bioinformatics 10 (January 2014): EBO.S13481. http://dx.doi.org/10.4137/ebo.s13481.

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Chen, Anthony, Chao Yang, Sirisak Kongsomsaksakul, and Ming Lee. "Network-based Accessibility Measures for Vulnerability Analysis of Degradable Transportation Networks." Networks and Spatial Economics 7, no. 3 (December 20, 2006): 241–56. http://dx.doi.org/10.1007/s11067-006-9012-5.

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Tanzi, Matteo, and Lai-Sang Young. "Existence of physical measures in some excitation–inhibition networks*." Nonlinearity 35, no. 2 (December 22, 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 subsystems. Appealing to existing techniques in (nonuniform) hyperbolic theory, we study their Lyapunov exponents and entropy, and prove that large time network dynamics are governed by physical measures with the SRB property.

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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 (January 22, 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 and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory–inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.

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Wang, Bing, Zhiwen Sun, and Yuexing Han. "A Path-Based Distribution Measure for Network Comparison." Entropy 22, no. 11 (November 12, 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 distributions, we define and compare network distance between networks to measure how dissimilar the two networks are, and the network entropy to characterize a typical network system. We find that the network distance is able to discriminate networks generated by different models. Combining more information on end nodes along a path can further amplify the dissimilarity of networks. The network entropy is able to detect tipping points in the evolution of synthetic networks. Extensive numerical simulations reveal the effectivity of the proposed measure in network reduction of multilayer networks, and identification of typical system states in temporal networks as well.

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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 (December 11, 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 this, various measures of centrality have been developed, which make it possible to assess the degree of significance of the nodes of the network graph in the structure of the network under consideration. The existing variety of measures of centrality gives rise to the problem of choosing the one that most fully describes the significance and centrality of the node. The relevance of the work is due to the need to analyze the centrality measures to determine the significance of vertices, which is one of the main tasks of studying networks (graphs) in practical applications. The study made it possible, using the principal component method, to identify collinear measures of centrality, which can be further excluded both to reduce the computational complexity of calculations, which is especially important for networks that include a large number of nodes, and to increase the reliability of the interpretation of the results obtained when evaluating the significance node within the analyzed network in solving practical problems. In the course of the study, the patterns of representation of various measures of centrality in the space of principal components were revealed, which allow them to be classified in terms of the proximity of the images of network nodes formed in the space determined by the measures of centrality used.

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Pandey, Sakshi Dev, A. S. Ranadive, Sovan Samanta, and Biswajit Sarkar. "Bipolar-Valued Fuzzy Social Network and Centrality Measures." Discrete Dynamics in Nature and Society 2022 (June 29, 2022): 1–13. http://dx.doi.org/10.1155/2022/9713575.

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The concept of bipolar-valued fuzzy relationships and their role in modeling bipolar-valued fuzzy social networks (BVFSN) are discussed. The goal of this study is to represent the bipolarity and fuzziness that are always present in the relationship between actors. Ranking the most central actors to achieve maximum spreading ability has been a difficult and critical topic thus far. Many centrality measures have been proposed to determine the significance of nodes in the central person detection process. In this article, we proposed two new centrality measures, BF-degree centrality and BF-closeness centrality, based on the natural characteristics of a bipolar-valued fuzzy social network. The proposed centrality measures highlight the fuzziness and bipolarity of the relationship. We consider the fuzzy stability index for direct connections here, as well as the strengths and weaknesses of the relationship. With the help of the effective and total effective strength of the path, we find the favorable path in this centrality measure. Moreover, to investigate the validity and reliability of these new centrality measures, we gathered Google Scholar data and built a G-S Research network. Experimental results show that the model and centrality measures can be used to objectively rank the most central node in the network.

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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 redundancy of metro network is evaluated by statistical indicators of the distribution of the O-D-level number of effective routes. Compared with the existing connectivity and accessibility measures of topology network performance, route redundancy is also based on the topology network, but it takes the travellers’ route choice into consideration. Specifically, the effective routes between each O-D pair would provide disaggregated information from the travellers’ perspective. Case studies in four metropolises in the world, i.e., Shanghai, Beijing, London, and Tokyo, are conducted to examine the predisaster preparedness of the four metro networks explicitly from the perspective of route redundancy. The results indicate that the London metro network has the best route redundancy performance in terms of the statistical indicators of the distribution of the O-D level number of effective routes. Furthermore, the results of route redundancy are compared with typical measures of topology network performance in terms of measuring connectivity and accessibility of metro networks. Their differences are attributed to the fact that the route redundancy measure considers the travellers’ O-D-level route choice beyond the pure network topology and the shortest path considerations of the existing measures. The route redundancy proposed in this paper could assist in evaluating the predisaster preparedness of current or planning metro networks from O-D level to network level.

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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (December 31, 2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.

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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks in USA, China, India, Italy and Spain. In the conclusion some suggestions, observations and perspective dealing with complex network in transportation networks have been presented.

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Lee, Jeong Won, and Kang Won Lee. "Analysis of Seoul Metropolitan Subway Network Characteristics Using Network Centrality Measures." Journal of the Korean Society for Railway 20, no. 3 (June 30, 2017): 413–22. http://dx.doi.org/10.7782/jksr.2017.20.3.413.

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Popescu, Pantelimon-George, Florin Pop, Alexandru Herişanu, and Nicolae Ţăpuş. "New Inequalities between Information Measures of Network Information Content." Mathematical Problems in Engineering 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/175769.

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We refine a classical logarithmic inequality using a discrete case of Bernoulli inequality, and then we refine furthermore two information inequalities between information measures for graphs, based on information functionals, presented by Dehmer and Mowshowitz in (2010) as Theorems 4.7 and 4.8. The inequalities refer to entropy-based measures of network information content and have a great impact for information processing in complex networks (a subarea of research in modeling of complex systems).

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Mandell, Myrna, and Robyn Keast. "Evaluating Network Arrangements: Toward Revised Performance Measures." Public Performance & Management Review 30, no. 4 (June 1, 2007): 574–97. http://dx.doi.org/10.2753/pmr1530-9576300406.

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Yamashita, Kazuyuki, Yuichi Yasuda, Ryo Nakamura, and Hiroyuki Ohsaki. "Predictability of Network Robustness from Spectral Measures." Journal of Information Processing 28 (2020): 551–61. http://dx.doi.org/10.2197/ipsjjip.28.551.

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Gulhan, Gorkem, Huseyin Ceylan, and Halim Ceylan. "Using accessibility measures in transit network design." Transport 33, no. 2 (April 12, 2017): 510–19. http://dx.doi.org/10.3846/16484142.2017.1295401.

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Transit planning scenarios may lead to the different Objective Function (OF) values since each scenario has different transit travel times, frequencies and fleet sizes. Change on those variables leads to the different accessibility values for each route set. Therefore, the actual performance of a route set may be unforeseen since the accessibility values are out of evaluation criteria. This study tries to generate techniques, which handle the relation between accessibility and transportation in the scope of public transit. The accessibility measures, which have direct relation with land use and transportation, are utilized in transit route set decision. Accessibility measures have been utilized in the decision-making process of transit network design. Conventional OFs, which are used to determine the most effective route sets are combined with accessibility based OFs and the decision-making process of transit network design is strengthened. In this context, the effects of accessibility measures in decision-making process of transit network design have been represented on an 8-node example transit network. The results showed the accessibility measures could effectively improve the planners’ decision accuracy.

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Guzman, Joshua D., Richard F. Deckro, Matthew J. Robbins, James F. Morris, and Nicholas A. Ballester. "An Analytical Comparison of Social Network Measures." IEEE Transactions on Computational Social Systems 1, no. 1 (March 2014): 35–45. http://dx.doi.org/10.1109/tcss.2014.2307451.

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Journal articles on the topic 'Network measures'

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