Relevant bibliographies by topics / Clustering coefficient

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Academic literature on the topic 'Clustering coefficient'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Clustering coefficient"

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Bloznelis, Mindaugas, and Valentas Kurauskas. "Clustering function: another view on clustering coefficient." Journal of Complex Networks 4, no. 1 (2015): 61–86. http://dx.doi.org/10.1093/comnet/cnv010.

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Yu, Pei, Qiang Guo, Ren-De Li, Jing-Ti Han, and Jian-Guo Liu. "Roles of clustering properties for degree-mixing pattern networks." International Journal of Modern Physics C 28, no. 03 (2017): 1750029. http://dx.doi.org/10.1142/s0129183117500292.

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The clustering coefficients have been extensively investigated for analyzing the local structural properties of complex networks. In this paper, the clustering coefficients for triangle and square structures, namely [Formula: see text] and [Formula: see text], are introduced to measure the local structure properties for different degree-mixing pattern networks. Firstly, a network model with tunable assortative coefficients is introduced. Secondly, the comparison results between the local clustering coefficients [Formula: see text] and [Formula: see text] are reported, one can find that the squ
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MATSUO, Yutaka. "Clustering Algorithm by Graph Partition using Clustering Coefficient." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 15, no. 3 (2003): 318–22. http://dx.doi.org/10.3156/jsoft.15.318.

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Schank, Thomas, and Dorothea Wagner. "Approximating Clustering Coefficient and Transitivity." Journal of Graph Algorithms and Applications 9, no. 2 (2005): 265–75. http://dx.doi.org/10.7155/jgaa.00108.

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Ruan, Yuhong, and Anwei Li. "Influence of Dynamical Change of Edges on Clustering Coefficients." Discrete Dynamics in Nature and Society 2015 (2015): 1–5. http://dx.doi.org/10.1155/2015/172720.

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Clustering coefficient is a very important measurement in complex networks, and it describes the average ratio between the actual existent edges and probable existent edges in the neighbor of one vertex in a complex network. Besides, in a complex networks, the dynamic change of edges can trigger directly the evolution of network and further affect the clustering coefficients. As a result, in this paper, we investigate the effects of the dynamic change of edge on the clustering coefficients. It is illustrated that the increase and decrease of the clustering coefficient can be effectively contro
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Pedersen, Mangor, Amir Omidvarnia, Jennifer M. Walz, Andrew Zalesky, and Graeme D. Jackson. "Spontaneous brain network activity: Analysis of its temporal complexity." Network Neuroscience 1, no. 2 (2017): 100–115. http://dx.doi.org/10.1162/netn_a_00006.

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The brain operates in a complex way. The temporal complexity underlying macroscopic and spontaneous brain network activity is still to be understood. In this study, we explored the brain’s complexity by combining functional connectivity, graph theory, and entropy analyses in 25 healthy people using task-free functional magnetic resonance imaging. We calculated the pairwise instantaneous phase synchrony between 8,192 brain nodes for a total of 200 time points. This resulted in graphs for which time series of clustering coefficients (the “cliquiness” of a node) and participation coefficients (th
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Liu, Xiao-Lu, Shu-Wei Jia, and Yan Gu. "Empirical analysis of the user reputation and clustering property for user-object bipartite networks." International Journal of Modern Physics C 30, no. 05 (2019): 1950035. http://dx.doi.org/10.1142/s0129183119500359.

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User reputation is of great significance for online rating systems which can be described by user-object bipartite networks, measuring the user ability of rating accurate assessments of various objects. The clustering coefficients have been widely investigated to analyze the local structural properties of complex networks, analyzing the diversity of user interest. In this paper, we empirically analyze the relation of user reputation and clustering property for the user-object bipartite networks. Grouping by user reputation, the results for the MovieLens dataset show that both the average clust
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Cooksey, Ray W., and Geoffrey N. Soutar. "Coefficient Beta and Hierarchical Item Clustering." Organizational Research Methods 9, no. 1 (2006): 78–98. http://dx.doi.org/10.1177/1094428105283939.

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Wu, Zhihao, Youfang Lin, Jing Wang, and Steve Gregory. "Link prediction with node clustering coefficient." Physica A: Statistical Mechanics and its Applications 452 (June 2016): 1–8. http://dx.doi.org/10.1016/j.physa.2016.01.038.

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Gentner, Michael, Irene Heinrich, Simon Jäger, and Dieter Rautenbach. "Large values of the clustering coefficient." Discrete Mathematics 341, no. 1 (2018): 119–25. http://dx.doi.org/10.1016/j.disc.2017.08.020.

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Dissertations / Theses on the topic "Clustering coefficient"

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Parikh, Nidhi Kiranbhai. "Generating Random Graphs with Tunable Clustering Coefficient." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31591.

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Most real-world networks exhibit a high clustering coefficientâ the probability that two neighbors of a node are also neighbors of each other. We propose four algorithms CONF-1, CONF-2, THROW-1, and THROW-2 which are based on the configuration model and that take triangle degree sequence (representing the number of triangles/corners at a node) and single-edge degree sequence (representing the number of single-edges/stubs at a node) as input and generate a random graph with a tunable clustering coefficient. We analyze them theoretically and empirically for the case of a regular graph. CONF-1 a
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Jäger, Simon [Verfasser]. "Exponential domination, exponential independence, and the clustering coefficient / Simon Jäger." Ulm : Universität Ulm, 2017. http://d-nb.info/114748449X/34.

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Heinrich, Irene [Verfasser]. "On Graph Decomposition: Hajós' Conjecture, the Clustering Coefficient and Dominating Sets / Irene Heinrich." München : Verlag Dr. Hut, 2020. http://d-nb.info/1219606197/34.

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Oppong, Augustine. "Clustering Mixed Data: An Extension of the Gower Coefficient with Weighted L2 Distance." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3463.

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Sorting out data into partitions is increasing becoming complex as the constituents of data is growing outward everyday. Mixed data comprises continuous, categorical, directional functional and other types of variables. Clustering mixed data is based on special dissimilarities of the variables. Some data types may influence the clustering solution. Assigning appropriate weight to the functional data may improve the performance of the clustering algorithm. In this paper we use the extension of the Gower coefficient with judciously chosen weight for the L2 to cluster mixed data.The benefits of w
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Nascimento, Mariá Cristina Vasconcelos. "Metaheurísticas para o problema de agrupamento de dados em grafo." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17052010-155334/.

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O problema de agrupamento de dados em grafos consiste em encontrar clusters de nós em um dado grafo, ou seja, encontrar subgrafos com alta conectividade. Esse problema pode receber outras nomenclaturas, algumas delas são: problema de particionamento de grafos e problema de detecção de comunidades. Para modelar esse problema, existem diversas formulações matemáticas, cada qual com suas vantagens e desvantagens. A maioria dessas formulações tem como desvantagem a necessidade da definição prévia do número de grupos que se deseja obter. Entretanto, esse tipo de informação não está contida em dados
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Koomson, Obed. "Performance Assessment of The Extended Gower Coefficient on Mixed Data with Varying Types of Functional Data." Digital Commons @ East Tennessee State University, 2018. https://dc.etsu.edu/etd/3512.

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Clustering is a widely used technique in data mining applications to source, manage, analyze and extract vital information from large amounts of data. Most clustering procedures are limited in their performance when it comes to data with mixed attributes. In recent times, mixed data have evolved to include directional and functional data. In this study, we will give an introduction to clustering with an eye towards the application of the extended Gower coefficient by Hendrickson (2014). We will conduct a simulation study to assess the performance of this coefficient on mixed data whose functio
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Li, Han. "Statistical Modeling and Analysis of Bivariate Spatial-Temporal Data with the Application to Stream Temperature Study." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/70862.

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Water temperature is a critical factor for the quality and biological condition of streams. Among various factors affecting stream water temperature, air temperature is one of the most important factors related to water temperature. To appropriately quantify the relationship between water and air temperatures over a large geographic region, it is important to accommodate the spatial and temporal information of the steam temperature. In this dissertation, I devote effort to several statistical modeling techniques for analyzing bivariate spatial-temporal data in a stream temperature study. I
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Stephens, Skylar Nicholas. "Analytical and Computational Micromechanics Analysis of the Effects of Interphase Regions, Orientation, and Clustering on the Effective Coefficient of Thermal Expansion of Carbon Nanotube-Polymer Nanocomposites." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23216.

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Analytic and computational micromechanics techniques based on the composite cylinders method and the finite element method, respectively, have been used to determine the effective coefficient of thermal expansion (CTE) of carbon nanotube-epoxy nanocomposites containing aligned nanotubes. Both techniques have been used in a parametric study of the influence of interphase stiffness and interphase CTE on the effective CTE of the nanocomposites.  For both the axial and transverse CTE of aligned nanotube nanocomposites with and without interphase regions, the computational and analytic micromechani
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Dhanasetty, Abhishek. "Enumerating Approximate Maximal Cliques in a Distributed Framework." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617104719399743.

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Lee, James H. "A pollination network of Cornus florida." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3615.

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From the agent-based, correlated random walk model presented, we observe the effects of varying the parameter values of maximum insect turning area, 𝛿max, density of trees, ω, maximum pollen carryover, 𝜅max, and probability of fertilization, P𝜅, on the distribution of pollen within a population of Cornus florida (flowering dogwood). We see that varying 𝛿max and 𝜅max changes the dispersal distance of pollen, which greatly affects many measures of connectivity. The clustering coefficient of fathers is maximized when 𝛿max is between 60° and 90°. Varying ω does not have a major effect on the clust
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Books on the topic "Clustering coefficient"

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Bianconi, Ginestra. Basic Structural Properties. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.003.0006.

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In this chapter the basic structural properties of multilayer networks are given. This chapter reveals that on multilayer networks the most basic structural properties of a network such as the degree or the clustering coefficient are also significantly modified. Therefore, it is necessary to define the multiplex degree and the multiplex degree distribution, the multilayer degree and the multilayer degree distribution, and the multilayer clustering coefficients. The chapter also discusses the relation between the properties of multiplex and multi-slice networks and the corresponding properties
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Newman, Mark. Measures and metrics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0007.

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This chapter describes the measures and metrics that are used to quantify network structure. The chapter starts with a discussion of centrality measures, which are used to identify central or important nodes in networks. Measures discussed include degree centrality, eigenvector centrality, PageRank, closeness, and betweenness. This is followed by a discussion of groupings of nodes like cliques and components, transitivity measures including the clustering coefficient, structural balance in networks, similarity measures, and assortative mixing.
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Newman, Mark. Random graphs. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0011.

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An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a dis
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Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.

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A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, an
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Coolen, A. C. C., A. Annibale, and E. S. Roberts. Definitions and concepts. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0002.

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A network is specified by its links and nodes. However, it can be described by a much wider range of interesting and important topological features. This chapter introduces how a network can be characterized by its microscopic topological features and macroscopic topological features. Microscopic features introduced are degree and clustering coefficients. Macroscopic topological features introduced are the degree distribution; correlation between degrees of connected nodes; modularity; and, the eigenvalue spectrum (which counts the number of closed paths in the graph).
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Book chapters on the topic "Clustering coefficient"

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Chalancon, Guilhem, Kai Kruse, and M. Madan Babu. "Clustering Coefficient." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1239.

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Zhong, MingJie, ZhiJun Ding, HaiChun Sun, and PengWei Wang. "A Self-learning Clustering Algorithm Based on Clustering Coefficient." In Web Information Systems Engineering – WISE 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11749-2_6.

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Batagelj, Vladimir. "Corrected Overlap Weight and Clustering Coefficient." In Lecture Notes in Social Networks. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31463-7_1.

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Brautbar, Michael, and Michael Kearns. "A Clustering Coefficient Network Formation Game." In Algorithmic Game Theory. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24829-0_21.

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Pattanayak, Himansu Sekhar, Harsh K. Verma, and A. L. Sangal. "Relationship Between Community Structure and Clustering Coefficient." In Intelligent Computing and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5566-4_18.

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Lattanzi, Silvio, and Stefano Leonardi. "Efficient Computation of the Weighted Clustering Coefficient." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13123-8_4.

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Ostroumova Prokhorenkova, Liudmila, and Egor Samosvat. "Global Clustering Coefficient in Scale-Free Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13123-8_5.

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Silva, Osvaldo, Áurea Sousa, and Helena Bacelar-Nicolau. "Clustering Validation in the Context of Hierarchical Cluster Analysis: An Empirical Study." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_37.

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AbstractThe evaluation of clustering structures is a crucial step in cluster analysis. This study presents the main results of the hierarchical cluster analysis of variables concerning a real dataset in the context of Higher Education. The goal of this research is to find a typology of some relevant items taking into account both the homogeneity and the isolation of the clusters. Two similarity measures, namely the standard affinity coefficient and Spearman’s correlation coefficient, were used, and combined with three probabilistic (AVL, AVB and AV1) aggregation criteria, from a parametric fam
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Ardickas, Daumilas, and Mindaugas Bloznelis. "Clustering Coefficient of a Preferred Attachment Affiliation Network." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48478-1_6.

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Krot, Alexander, and Liudmila Ostroumova Prokhorenkova. "Local Clustering Coefficient in Generalized Preferential Attachment Models." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26784-5_2.

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Conference papers on the topic "Clustering coefficient"

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Ding, Hongfa, Peiwang Fu, Yingxuan Luo, Heling Jiang, and Hai Liu. "Collecting Clustering Coefficient of Distributed Graph Data with Shuffled Differential Privacy." In 2024 IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA). IEEE, 2024. https://doi.org/10.1109/ispa63168.2024.00103.

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Selvarani, S., and R. Catherin Ida Shylu. "Mutual Clustering Coefficient-Based Suspicious-Link Detection Enhancing Sentiment Analysis in Social Networks." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894246.

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Taibi, Salaheddine, Salim Bouamama, and Lyazid Toumi. "An Enhanced Bat Algorithm Using Clustering Coefficient for Community Detection in Complex Networks." In 2024 1st International Conference on Innovative and Intelligent Information Technologies (IC3IT). IEEE, 2024. https://doi.org/10.1109/ic3it63743.2024.10869406.

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Rui Zhang, Lei Li, Chongming Bao, Lihua Zhou, and Bing Kong. "The community detection algorithm based on the node clustering coefficient and the edge clustering coefficient." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7053250.

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Du, Cai-Feng. "High Clustering Coefficient of Computer Networks." In 2009 WASE International Conference on Information Engineering (ICIE). IEEE, 2009. http://dx.doi.org/10.1109/icie.2009.276.

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Green, Oded, and David A. Bader. "Faster Clustering Coefficient Using Vertex Covers." In 2013 International Conference on Social Computing (SocialCom). IEEE, 2013. http://dx.doi.org/10.1109/socialcom.2013.51.

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Bhatia, Siddharth. "Approximate Triangle Count and Clustering Coefficient." In SIGMOD/PODS '18: International Conference on Management of Data. ACM, 2018. http://dx.doi.org/10.1145/3183713.3183715.

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Baozhi Qiu, Chenke Jia, and Junyi Shen. "Local Outlier Coefficient-Based Clustering Algorithm." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1714201.

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Etemadi, Roohollah, and Jianguo Lu. "Bias correction in clustering coefficient estimation." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8257976.

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Bai Jinbo, Li Hongbo, and Chu Yan. "Community identification based on clustering coefficient." In 2011 6th International ICST Conference on Communications and Networking in China (CHINACOM). IEEE, 2011. http://dx.doi.org/10.1109/chinacom.2011.6158261.

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Reports on the topic "Clustering coefficient"

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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez, and Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/hejw8941https://www.orangesparkleball.com/innovation-library-blog/2024/5/30/sunbelt2024-network-analysis-of-a-mobility-ecosystem-in-detroit-mi.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition,
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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez, and Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, 2024. http://dx.doi.org/10.61152/hejw8941.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition,
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