Relevant bibliographies by topics / Clustering coefficient

Academic literature on the topic 'Clustering coefficient'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Clustering coefficient.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Clustering coefficient"

1

Bloznelis, Mindaugas, and Valentas Kurauskas. "Clustering function: another view on clustering coefficient." Journal of Complex Networks 4, no. 1 (April 13, 2015): 61–86. http://dx.doi.org/10.1093/comnet/cnv010.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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 (March 2017): 1750029. http://dx.doi.org/10.1142/s0129183117500292.

Full text
Abstract:
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 square structures would increase as the degree [Formula: see text] of nodes increasing in disassortative networks. At the same time, the Pearson coefficient [Formula: see text] between the clustering coefficients [Formula: see text] and [Formula: see text] is calculated for networks with different assortative coefficients. The Pearson coefficient [Formula: see text] changes from [Formula: see text] to 0.98 as the assortative coefficient [Formula: see text] increasing from [Formula: see text] to 0.45, which suggests that the triangle and square structures have the same growth trend in assortative networks whereas the opposite one in disassortative networks. Finally, we analyze the clustering coefficients [Formula: see text] and [Formula: see text] for networks with tunable assortative coefficients and find that the clustering coefficient [Formula: see text] increases from 0.0038 to 0.5952 while the clustering coefficient [Formula: see text] increases from 0.00039 to 0.005, indicating that the number of cliquishness of the disassortative networks is larger than that of assortative networks.
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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 controlled by adding or deleting several edges of the network in the evolution of complex networks.
APA, Harvard, Vancouver, ISO, and other styles
6

Cooksey, Ray W., and Geoffrey N. Soutar. "Coefficient Beta and Hierarchical Item Clustering." Organizational Research Methods 9, no. 1 (January 2006): 78–98. http://dx.doi.org/10.1177/1094428105283939.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

黄, 子轩. "Link Prediction Based on Clustering Coefficient." Applied Physics 04, no. 06 (2014): 101–6. http://dx.doi.org/10.12677/app.2014.46014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Pandove, Divya, Shivani Goel, and Rinkle Rani. "General correlation coefficient based agglomerative clustering." Cluster Computing 22, no. 2 (November 2, 2018): 553–83. http://dx.doi.org/10.1007/s10586-018-2863-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Clustering coefficient"

1

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

Full text
Abstract:
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 and CONF-2 generate a random graph with the degree sequence and the clustering coefficient anticipated from the input triangle and single-edge degree sequences. At each time step, CONF-1 chooses each node for creating triangles or single edges with the same probability, while CONF-2 chooses a node for creating triangles or single edge with a probability proportional to their number of unconnected corners or unconnected stubs, respectively. Experimental results match quite well with the anticipated clustering coefficient except for highly dense graphs, in which case the experimental clustering coefficient is higher than the anticipated value. THROW-2 chooses three distinct nodes for creating triangles and two distinct nodes for creating single edges, while they need not be distinct for THROW-1. For THROW-1 and THROW-2, the degree sequence and the clustering coefficient of the generated graph varies from the input. However, the expected degree distribution, and the clustering coefficient of the generated graph can also be predicted using analytical results. Experiments show that, for THROW-1 and THROW-2, the results match quite well with the analytical results. Typically, only information about degree sequence or degree distribution is available. We also propose an algorithm DEG that takes degree sequence and clustering coefficient as input and generates a graph with the same properties. Experiments show results for DEG that are quite similar to those for CONF-1 and CONF-2.
Master of Science
APA, Harvard, Vancouver, ISO, and other styles
2

Jäger, Simon [Verfasser]. "Exponential domination, exponential independence, and the clustering coefficient / Simon Jäger." Ulm : Universität Ulm, 2017. http://d-nb.info/114748449X/34.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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.

Full text
Abstract:
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 weighting are demonstrated both in in applications to the Buoy data set as well simulation studies. Our studies show that clustering algorithms with application of proper weight give superior recovery level when a set of data with mixed continuous, categorical directional and functional attributes is clustered. We discuss open problems for future research in clustering mixed data.
APA, Harvard, Vancouver, ISO, and other styles
5

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/.

Full text
Abstract:
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 para agrupamento, ou seja, em dados não rotulados. Esse foi um dos motivos da popularização nas últimas décadas da medida conhecida como modularidade, que tem sido maximizada para encontrar partições em grafos. Essa formulação, além de não exigir a definição prévia do número de clusters, se destaca pela qualidade das partições que ela fornece. Nesta Tese, metaheurísticas Greedy Randomized Search Procedures para dois modelos existentes para agrupamento em grafos foram propostas: uma para o problema de maximização da modularidade e a outra para o problema de maximização da similaridade intra-cluster. Os resultados obtidos por essas metaheurísticas foram melhores quando comparadas àqueles de outras heurísticas encontradas na literatura. Entretanto, o custo computacional foi alto, principalmente o da metaheurística para o modelo de maximização da modularidade. Com o passar dos anos, estudos revelaram que a formulação que maximiza a modularidade das partições possui algumas limitações. A fim de promover uma alternativa à altura do modelo de maximização da modularidade, esta Tese propõe novas formulações matemáticas de agrupamento em grafos com e sem pesos que visam encontrar partições cujos clusters apresentem alta conectividade. Além disso, as formulações propostas são capazes de prover partições sem a necessidade de definição prévia do número de clusters. Testes com centenas de grafos com pesos comprovaram a eficiência dos modelos propostos. Comparando as partições provenientes de todos os modelos estudados nesta Tese, foram observados melhores resultados em uma das novas formulações propostas, que encontrou partições bastante satisfatórias, superiores às outras existentes, até mesmo para a de maximização de modularidade. Os resultados apresentaram alta correlação com a classificação real dos dados simulados e reais, sendo esses últimos, em sua maioria, de origem biológica
Graph clustering aims at identifying highly connected groups or clusters of nodes of a graph. This problem can assume others nomenclatures, such as: graph partitioning problem and community detection problem. There are many mathematical formulations to model this problem, each one with advantages and disadvantages. Most of these formulations have the disadvantage of requiring the definition of the number of clusters in the final partition. Nevertheless, this type of information is not found in graphs for clustering, i.e., whose data are unlabeled. This is one of the reasons for the popularization in the last decades of the measure known as modularity, which is being maximized to find graph partitions. This formulation does not require the definition of the number of clusters of the partitions to be produced, and produces high quality partitions. In this Thesis, Greedy Randomized Search Procedures metaheuristics for two existing graph clustering mathematical formulations are proposed: one for the maximization of the partition modularity and the other for the maximization of the intra-cluster similarity. The results obtained by these proposed metaheuristics outperformed the results from other heuristics found in the literature. However, their computational cost was high, mainly for the metaheuristic for the maximization of modularity model. Along the years, researches revealed that the formulation that maximizes the modularity of the partitions has some limitations. In order to promote a good alternative for the maximization of the partition modularity model, this Thesis proposed new mathematical formulations for graph clustering for weighted and unweighted graphs, aiming at finding partitions with high connectivity clusters. Furthermore, the proposed formulations are able to provide partitions without a previous definition of the true number of clusters. Computational tests with hundreds of weighted graphs confirmed the efficiency of the proposed models. Comparing the partitions from all studied formulations in this Thesis, it was possible to observe that the proposed formulations presented better results, even better than the maximization of partition modularity. These results are characterized by satisfactory partitions with high correlation with the true classification for the simulated and real data (mostly biological)
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
Abstract:
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 functional component has strictly-decreasing signal curves and also those whose functional component has a mixture of strictly-decreasing signal curves and periodic tendencies. We will assess how four different hierarchical clustering algorithms perform on mixed data simulated under varying conditions with and without weights. The comparison of the various clustering solutions will be done using the Rand Index.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
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. In the first part, I focus our analysis on the individual stream. A time varying coefficient model (VCM) is used to study the relationship between air temperature and water temperature for each stream. The time varying coefficient model enables dynamic modeling of the relationship, and therefore can be used to enhance the understanding of water and air temperature relationships. The proposed model is applied to 10 streams in Maryland, West Virginia, Virginia, North Carolina and Georgia using daily maximum temperatures. The VCM approach increases the prediction accuracy by more than 50% compared to the simple linear regression model and the nonlinear logistic model. The VCM that describes the relationship between water and air temperatures for each stream is represented by slope and intercept curves from the fitted model. In the second part, I consider water and air temperatures for different streams that are spatial correlated. I focus on clustering multiple streams by using intercept and slope curves estimated from the VCM. Spatial information is incorporated to make clustering results geographically meaningful. I further propose a weighted distance as a dissimilarity measure for streams, which provides a flexible framework to interpret the clustering results under different weights. Real data analysis shows that streams in same cluster share similar geographic features such as solar radiation, percent forest and elevation. In the third part, I develop a spatial-temporal VCM (STVCM) to deal with missing data. The STVCM takes both spatial and temporal variation of water temperature into account. I develop a novel estimation method that emphasizes the time effect and treats the space effect as a varying coefficient for the time effect. A simulation study shows that the performance of the STVCM on missing data imputation is better than several existing methods such as the neural network and the Gaussian process. The STVCM is also applied to all 156 streams in this study to obtain a complete data record.
Ph. D.
APA, Harvard, Vancouver, ISO, and other styles
8

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.

Full text
Abstract:
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 micromechanics techniques were shown to give similar results.  The Mori-Tanka method has been used to account for the effect of randomly oriented fibers.   Analytic and computational micromechanics techniques have also been used to assess the effects of clustering and clustering with interphase on the effective CTE components.  Clustering is observed to have a minimal impact on the effective axial CTE of the nanocomposite and a 3-10%.  However, there is a combined effect with clustering and one of the interphase layers.
Master of Science
APA, Harvard, Vancouver, ISO, and other styles
9

Dhanasetty, Abhishek. "Enumerating Approximate Maximal Cliques in a Distributed Framework." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617104719399743.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

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

Full text
Abstract:
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 clustering coefficient of fathers, but it does have a greater effect on other measures of genetic diversity. Lastly, we compare our simulations with randomly-placed trees with that of actual tree placement of C. florida at the VCU Rice Center, concluding that in order to truly understand how pollen is distributed within a specific ecosystem, specificity in describing tree locations is necessary.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Clustering coefficient"

1

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

Full text
Abstract:
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 of their aggregated network. Finally, the chapter introduces multilayer distance-dependent measures, including generalization of the betweenness centrality (interdependence, cross-betweenness) and of the closeness centrality.
APA, Harvard, Vancouver, ISO, and other styles
2

Newman, Mark. Measures and metrics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0007.

Full text
Abstract:
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.
APA, Harvard, Vancouver, ISO, and other styles
3

Newman, Mark. Random graphs. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0011.

Full text
Abstract:
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 discussion of some of the shortcomings of the random graph model.
APA, Harvard, Vancouver, ISO, and other styles
4

Newman, Mark. The configuration model. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198805090.003.0012.

Full text
Abstract:
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, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
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).
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Clustering coefficient"

1

Chalancon, Guilhem, Kai Kruse, and M. Madan Babu. "Clustering Coefficient." In Encyclopedia of Systems Biology, 422–24. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_1239.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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, 79–94. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11749-2_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Batagelj, Vladimir. "Corrected Overlap Weight and Clustering Coefficient." In Lecture Notes in Social Networks, 1–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31463-7_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Brautbar, Michael, and Michael Kearns. "A Clustering Coefficient Network Formation Game." In Algorithmic Game Theory, 224–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24829-0_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Pattanayak, Himansu Sekhar, Harsh K. Verma, and A. L. Sangal. "Relationship Between Community Structure and Clustering Coefficient." In Intelligent Computing and Applications, 203–20. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5566-4_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lattanzi, Silvio, and Stefano Leonardi. "Efficient Computation of the Weighted Clustering Coefficient." In Lecture Notes in Computer Science, 34–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13123-8_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Ostroumova Prokhorenkova, Liudmila, and Egor Samosvat. "Global Clustering Coefficient in Scale-Free Networks." In Lecture Notes in Computer Science, 47–58. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13123-8_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Ardickas, Daumilas, and Mindaugas Bloznelis. "Clustering Coefficient of a Preferred Attachment Affiliation Network." In Lecture Notes in Computer Science, 82–95. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48478-1_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Krot, Alexander, and Liudmila Ostroumova Prokhorenkova. "Local Clustering Coefficient in Generalized Preferential Attachment Models." In Lecture Notes in Computer Science, 15–28. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26784-5_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Liu, Zhiyu, Chen Wang, Qiong Zou, and Huayong Wang. "Clustering Coefficient Queries on Massive Dynamic Social Networks." In Web-Age Information Management, 115–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14246-8_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Clustering coefficient"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

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

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Zongwei Jia, Jun Cui, and Wei Li. "Clustering graph based on Edge Linking Coefficient." In 2010 International Conference on Computer Application and System Modeling (ICCASM 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccasm.2010.5620499.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gupta, Anand Kumar, and Neetu Sardana. "Significance of Clustering Coefficient over Jaccard Index." In 2015 Eighth International Conference on Contemporary Computing (IC3). IEEE, 2015. http://dx.doi.org/10.1109/ic3.2015.7346726.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Zhang, Z., L. Wang, Q. Su, Bingchao Ding, X. Xu, C. Wang, M. Xie, and P. Zhang. "Improved spectral clustering based on silhouette coefficient." In 9th International Symposium on Test Automation & Instrumentation (ISTAI 2022). Institution of Engineering and Technology, 2022. http://dx.doi.org/10.1049/icp.2022.3268.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Clustering coefficient' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography