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Journal articles on the topic 'Clustering coefficient'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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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.

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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.

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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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6

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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7

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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8

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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9

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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10

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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11

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.

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12

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

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13

Павлов, Юрий Леонидович, and Yury Pavlov. "On the Internet-graph clustering coefficient." Proceedings of the Karelian Research Centre of the Russian Academy of Sciences, no. 4 (June 23, 2023): 50. http://dx.doi.org/10.17076/mat1765.

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14

Aguilar-Alarcón, Jhon J., Juan C. Hernández-Gómez, and Jesús Romero-Valencia. "The Clustering Coefficient for Graph Products." Axioms 12, no. 10 (2023): 968. http://dx.doi.org/10.3390/axioms12100968.

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The clustering coefficient of a vertex v, of a degree of at least 2, in a graph Γ is obtained using the formula C(v)=2t(v)deg(v)(deg(v)−1), where t(v) denotes the number of triangles of the graph containing v as a vertex, and the clustering coefficient of Γ is defined as the average of the clustering coefficient of all vertices of Γ, that is, C(Γ)=1|V|∑v∈VC(v), where V is the vertex set of the graph. In this paper, we give explicit expressions for the clustering coefficient of corona and lexicographic products, as well as for the Cartesian sum; such expressions are given in terms of the order

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15

MCASSEY, MICHAEL P., and FETSJE BIJMA. "A clustering coefficient for complete weighted networks." Network Science 3, no. 2 (2015): 183–95. http://dx.doi.org/10.1017/nws.2014.26.

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AbstractThe clustering coefficient is typically used as a measure of the prevalence of node clusters in a network. Various definitions for this measure have been proposed for the cases of networks having weighted edges which may or not be directed. However, these techniques consistently assume that only a subset of all possible edges is present in the network, whereas there are weighted networks of interest in which all possible edges are present, that is, complete weighted networks. For this situation, the concept of clustering is redefined, and computational techniques are presented for comp

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16

Mogaraju, Jagadish Kumar. "Agglomerative and Divisive hierarchical cluster analysis of groundwater quality variables using opensource tools over YSR district, AP, India." Journal of Scientific Research 66, no. 04 (2022): 15–20. http://dx.doi.org/10.37398/jsr.2022.660403.

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Groundwater quality variables like F, Total Hardness (TH), Total Alkalinity (TA), Total Dissolved Solids (TDS), SO4, SAR, NA, EC, Cl, Ca, Mg, and pH were tested with Hierarchical clustering analysis (HCA) to identify the groupings or clusters that exist in the dataset. The dataset is subjected to Agglomerative and divisive hierarchical clustering. The observations were scaled to compare variables systematically. The clustering structure was determined using an agglomerative coefficient. Agglomerative approaches like complete, average, single, and ward are tested using agglomerative coefficient

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17

Goldstein, Rutherford, and Michael S. Vitevitch. "Phonological neighborhood clustering coefficient influences word learning." Journal of the Acoustical Society of America 132, no. 3 (2012): 2076. http://dx.doi.org/10.1121/1.4755655.

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18

Santiago, Caio, Vivian Pereira, and Luciano Digiampietri. "Homology Detection Using Multilayer Maximum Clustering Coefficient." Journal of Computational Biology 25, no. 12 (2018): 1328–38. http://dx.doi.org/10.1089/cmb.2017.0266.

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19

Lattanzi, Silvio, and Stefano Leonardi. "Efficient computation of the Weighted Clustering Coefficient." Internet Mathematics 12, no. 6 (2016): 381–401. http://dx.doi.org/10.1080/15427951.2016.1198281.

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20

Paembonan, Solmin, and Hisma Abduh. "Penerapan Metode Silhouette Coefficient untuk Evaluasi Clustering Obat." PENA TEKNIK: Jurnal Ilmiah Ilmu-Ilmu Teknik 6, no. 2 (2021): 48. http://dx.doi.org/10.51557/pt_jiit.v6i2.659.

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Dalam penelitian ini menggunakan metode k-means, metode ini dapat digunakan untuk menjadikan beberapa obat yang mirip menjadi suatu kelompok data tertentu. Salah satu cara untuk mengetahui tingkat kemiripan data adalah melalui perhitungan jarak antar data. Semakain kecil jarak antar data semakin tinggi tingkat kemiripan data tersebut dan sebaliknya semakin besar jarak antar data maka semakin rendah tingkat kemiripannya. Tujuan akhir clustering adalah untuk menentukan kelompok dalam sekumpulan data yang tidak berlabel, karena clustering merupakan suatu metode unsupervised dan tidak terdapat sua

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21

Wang, Zhen Bo, and Bao Zhi Qiu. "Fuzzy C-Means Clustering Algorithm Based on Coefficient of Variation." Advanced Materials Research 998-999 (July 2014): 873–77. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.873.

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To reduce the impact of irrelevant attributes on clustering results, and improve the importance of relevant attributes to clustering, this paper proposes fuzzy C-means clustering algorithm based on coefficient of variation (CV-FCM). In the algorithm, coefficient of variation is used to weigh attributes so as to assign different weights to each attribute in the data set, and the magnitude of weight is used to express the importance of different attributes to clusters. In addition, for the characteristic of fuzzy C-means clustering algorithm that it is susceptible to initial cluster center value

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22

Nascimento, Mariá C. V., and André C. P. L. F. Carvalho. "A graph clustering algorithm based on a clustering coefficient for weighted graphs." Journal of the Brazilian Computer Society 17, no. 1 (2010): 19–29. http://dx.doi.org/10.1007/s13173-010-0027-x.

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23

Rahman, Zahid, Altaf Hussain, Hussain Shah, and Muhammad Arshad. "Urdu News Clustering Using K-Mean Algorithm On The Basis Of Jaccard Coefficient And Dice Coefficient Similarity." ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 10, no. 4 (2022): 381–99. http://dx.doi.org/10.14201/adcaij2021104381399.

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Clustering is the unsupervised machine learning process that group data objects into clusters such that objects within the same cluster are highly similar to one another. Every day the quantity of Urdu text is increasing at a high speed on the internet. Grouping Urdu news manually is almost impossible, and there is an utmost need to device a mechanism which cluster Urdu news documents based on their similarity. Clustering Urdu news documents with accuracy is a research issue and it can be solved by using similarity techniques i.e., Jaccard and Dice coefficient, and clustering k-mean algorithm.

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24

Verma, Rohit Kumar, Rakesh Tiwari, and Pratik Singh Thakur. "Partition Coefficient and Partition Entropy in Fuzzy C Means Clustering." Journal of Scientific Research and Reports 29, no. 12 (2023): 1–6. http://dx.doi.org/10.9734/jsrr/2023/v29i121812.

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This paper offers a comprehensive exploration of partition validation functions, specifically focusing on partition coefficient and partition entropy within the realm of fuzzy clustering—an influential approach in the field of clustering datasets. While fuzzy clustering facilitates the classification of data points into multiple clusters, the pivotal tasks of determining the optimal number of clusters and evaluating the validity of the resultant clusters pose inherent challenges. The study addresses these challenges, contributing to the broader understanding of effective fuzzy clustering metho

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25

SARAÇLI, Sinan, and Murat AKŞİT. "Büyük Veride Hiyerarşik Kümeleme Yöntemlerinin Kofenetik Korelasyon Katsayısı ile Karşılaştırılması." Afyon Kocatepe University Journal of Sciences and Engineering 22, no. 3 (2022): 552–59. http://dx.doi.org/10.35414/akufemubid.1018302.

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The aim of this study is to compare hierarchical clustering methods by Cophenetic Correlation Coefficient (CCC) when there is a big data. For this purpose, after giving information about big data, clustering methods and CCC, analyzes are carried out for the related data set. The 2015 air travel consumer report, which was used in the application part of the study and published by the US Ministry of Transport, was used as big data. Libraries of the Python programming language installed on the Amazon cloud server, which includes open-source big data technologies, were used for data analysis. Sinc

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26

Oliveira, R. I., R. Ribeiro, and R. Sanchis. "Disparity of clustering coefficients in the Holme‒Kim network model." Advances in Applied Probability 50, no. 3 (2018): 918–43. http://dx.doi.org/10.1017/apr.2018.41.

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Abstract The Holme‒Kim random graph process is a variant of the Barabási‒Álbert scale-free graph that was designed to exhibit clustering. In this paper we show that whether the model does indeed exhibit clustering depends on how we define the clustering coefficient. In fact, we find that the local clustering coefficient typically remains positive whereas global clustering tends to 0 at a slow rate. These and other results are proven via martingale techniques, such as Freedman's concentration inequality combined with a bootstrapping argument.

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27

GUO, QIANG, RUI LENG, KERUI SHI, and JIAN-GUO LIU. "INFORMATION FILTERING VIA CLUSTERING COEFFICIENTS OF USER–OBJECT BIPARTITE NETWORKS." International Journal of Modern Physics C 23, no. 02 (2012): 1250012. http://dx.doi.org/10.1142/s012918311250012x.

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The clustering coefficient of user–object bipartite networks is presented to evaluate the overlap percentage of neighbors rating lists, which could be used to measure interest correlations among neighbor sets. The collaborative filtering (CF) information filtering algorithm evaluates a given user's interests in terms of his/her friends' opinions, which has become one of the most successful technologies for recommender systems. In this paper, different from the object clustering coefficient, users' clustering coefficients of user–object bipartite networks are introduced to improve the user simi

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28

Liu, Yongxin, Bin Song, Linong Wang, Jiachen Gao, and Rihong Xu. "Power Transformer Fault Diagnosis Based on Dissolved Gas Analysis by Correlation Coefficient-DBSCAN." Applied Sciences 10, no. 13 (2020): 4440. http://dx.doi.org/10.3390/app10134440.

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The transformers work in a complex environment, which makes them prone to failure. Dissolved gas analysis (DGA) is one of the most important methods for oil-immersed transformers’ internal insulation fault diagnosis. In view of the high correlation of the same fault data of transformers, this paper proposes a new method for transformers’ fault diagnosis based on correlation coefficient density clustering, which uses density clustering to extrapolate the correlation coefficient of DGA data. Firstly, we calculated the correlation coefficient of dissolved gas content in the fault transformers oil

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29

Altieri, Nicholas, Thomas Gruenenfelder, and David B. Pisoni. "Clustering coefficients of lexical neighborhoods." Mental Lexicon 5, no. 1 (2010): 1–21. http://dx.doi.org/10.1075/ml.5.1.01alt.

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High neighborhood density reduces the speed and accuracy of spoken word recognition. The two studies reported here investigated whether Clustering Coefficient (CC) — a graph theoretic variable measuring the degree to which a word’s neighbors are neighbors of one another, has similar effects on spoken word recognition. In Experiment 1, we found that high CC words were identified less accurately when spectrally degraded than low CC words. In Experiment 2, using a word repetition procedure, we observed longer response latencies for high CC words compared to low CC words. Taken together, the resul

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30

Zhang, Juping, Chan Yang, Zhen Jin, and Jia Li. "Dynamics analysis of SIR epidemic model with correlation coefficients and clustering coefficient in networks." Journal of Theoretical Biology 449 (July 2018): 1–13. http://dx.doi.org/10.1016/j.jtbi.2018.04.007.

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31

Nascimento, Ana Paula, Alexandra Oliveira, Brígida Mónica Faria, et al. "Affinity Coefficient for Clustering Autoregressive Moving Average Models." Computational and Mathematical Methods 2024 (May 24, 2024): 1–13. http://dx.doi.org/10.1155/2024/5540143.

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In various fields, such as economics, finance, bioinformatics, geology, and medicine, namely, in the cases of electroencephalogram, electrocardiogram, and biotechnology, cluster analysis of time series is necessary. The first step in cluster applications is to establish a similarity/dissimilarity coefficient between time series. This article introduces an extension of the affinity coefficient for the autoregressive expansions of the invertible autoregressive moving average models to measure their similarity between them. An application of the affinity coefficient between time series was develo

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32

Li, Pengyue, Liang Wei, Haiping Ding, Faxu Li, and Feng Hu. "Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient." Applied Sciences 13, no. 14 (2023): 8212. http://dx.doi.org/10.3390/app13148212.

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The structure of a model has an important impact on information dissemination. Many information models of hypernetworks have been proposed in recent years, in which nodes and hyperedges represent the individuals and the relationships between the individuals, respectively. However, these models select old nodes based on preference attachment and ignore the effect of aggregation. In real life, friends of friends are more likely to form friendships with each other, and a social network should be a hypernetwork with an aggregation phenomenon. Therefore, a social hypernetwork evolution model with a

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33

Kooij, Robert E., Nikolaj Horsevad Sørensen, and Roland Bouffanais. "Tuning the clustering coefficient of generalized circulant networks." Physica A: Statistical Mechanics and its Applications 578 (September 2021): 126088. http://dx.doi.org/10.1016/j.physa.2021.126088.

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34

Morita, Satoru. "Evolutionary game on networks with high clustering coefficient." Nonlinear Theory and Its Applications, IEICE 7, no. 2 (2016): 110–17. http://dx.doi.org/10.1587/nolta.7.110.

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35

LI, CHONG, SHI-ZE GUO, ZHE-MING LU, YU-LONG QIAO, and GUANG-HUA SONG. "A NEW CENTRALITY METRIC BASED ON CLUSTERING COEFFICIENT." International Journal of Modern Physics C 24, no. 07 (2013): 1350043. http://dx.doi.org/10.1142/s0129183113500435.

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Many centrality metrics have been proposed over the years to compute the centrality of nodes, which has been a key issue in complex network analysis. The most important node can be estimated through a variety of metrics, such as degree, closeness, eigenvector, betweenness, flow betweenness, cumulated nominations and subgraph. Simulated flow is a common method adopted by many centrality metrics, such as flow betweenness centrality, which assumes that the information spreads freely in the entire network. Generally speaking, the farther the information travels, the more times the information pass

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36

Iskhakov, L. N., M. S. Mironov, L. A. Prokhorenkova, B. Kamiński, and P. Prałat. "Clustering Coefficient of a Spatial Preferential Attachment Model." Doklady Mathematics 98, no. 1 (2018): 304–7. http://dx.doi.org/10.1134/s1064562418050046.

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37

Teraji, Tetsuro, and Norikazu Takahashi. "On Graphs that Locally Maximize Global Clustering Coefficient." IEICE Proceeding Series 2 (March 17, 2014): 130–33. http://dx.doi.org/10.15248/proc.2.130.

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38

Han, Jingti, and Changmei Mao. "Roles of Clustering Coefficient for the Network Reconstruction." Mathematical Problems in Engineering 2018 (October 24, 2018): 1–11. http://dx.doi.org/10.1155/2018/4949673.

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It is important to establish relations between the network reconstruction and the topological dynamical structure of networks. In this article, we quantify the effect for two types of network topologies on the performance of network reconstruction. First, we generate two network modes with variable clustering coefficient based on Holme-Kim model and Newman-Watts small-world model, then we reconstruct the artificial networks by using a novel framework called L1-norm minimization algorithm based on a theory called compressive sensing (CS), a framework for recovering sparse signals. The results o

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39

Rodríguez-Méndez, Victor, Enrico Ser-Giacomi, and Emilio Hernández-García. "Clustering coefficient and periodic orbits in flow networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 27, no. 3 (2017): 035803. http://dx.doi.org/10.1063/1.4971787.

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40

Zhang, Peng, Jinliang Wang, Xiaojia Li, Menghui Li, Zengru Di, and Ying Fan. "Clustering coefficient and community structure of bipartite networks." Physica A: Statistical Mechanics and its Applications 387, no. 27 (2008): 6869–75. http://dx.doi.org/10.1016/j.physa.2008.09.006.

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41

Li, Xuefei, Lijun Chang, Kai Zheng, Zi Huang, and Xiaofang Zhou. "Ranking weighted clustering coefficient in large dynamic graphs." World Wide Web 20, no. 5 (2016): 855–83. http://dx.doi.org/10.1007/s11280-016-0420-2.

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42

Gerhardt, Günther J. L., Ney Lemke, and Gilberto Corso. "Network clustering coefficient approach to DNA sequence analysis." Chaos, Solitons & Fractals 28, no. 4 (2006): 1037–45. http://dx.doi.org/10.1016/j.chaos.2005.08.138.

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43

Sai, L. Nitya, M. Sai Shreya, A. Anjan Subudhi, B. Jaya Lakshmi, and K. B. Madhuri. "Optimal K-Means Clustering Method Using Silhouette Coefficient." International Journal of Applied Research on Information Technology and Computing 8, no. 3 (2017): 335. http://dx.doi.org/10.5958/0975-8089.2017.00030.6.

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44

Liu, Xue, Xiaoping Zeng, Zhiming Wang, Bin Zhu, and Li Chen. "The Clustering Coefficient of Multiple Parallel Airlines AANET." International Journal of Future Generation Communication and Networking 9, no. 7 (2016): 135–44. http://dx.doi.org/10.14257/ijfgcn.2016.9.7.13.

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45

Ostroumova Prokhorenkova, Liudmila. "General results on preferential attachment and clustering coefficient." Optimization Letters 11, no. 2 (2016): 279–98. http://dx.doi.org/10.1007/s11590-016-1030-8.

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46

Fukami, Tatsuya, and Norikazu Takahashi. "New classes of clustering coefficient locally maximizing graphs." Discrete Applied Mathematics 162 (January 2014): 202–13. http://dx.doi.org/10.1016/j.dam.2013.09.013.

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47

Kumar, Ajay, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas. "Level-2 node clustering coefficient-based link prediction." Applied Intelligence 49, no. 7 (2019): 2762–79. http://dx.doi.org/10.1007/s10489-019-01413-8.

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48

Akhtar, Adil, and Tazid Ali. "Analysis of Unweighted Amino Acids Network." International Scholarly Research Notices 2014 (December 16, 2014): 1–6. http://dx.doi.org/10.1155/2014/350276.

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The analysis of amino acids network is very important to studying the various physicochemical properties of amino acids. In this paper we consider the amino acid network based on mutation of the codons. To analyze the relative importance of the amino acids we have discussed different measures of centrality. The measure of centrality is a powerful tool of graph theory for ranking the vertices and analysis of biological network. We have also investigated the correlation coefficients between various measures of centrality. Also we have discussed clustering coefficient as well as average clusterin

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49

Sudha, D., and S. Gowri. "Deep FC-IIWO Fuzzy Clustering: An Optimized Fuzzy Clustering Approach for Big Data Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 33, no. 03 (2025): 329–52. https://doi.org/10.1142/s021848852550014x.

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A significant role is played by clustering approaches in data mining processes, which becomes more challenging because of the rising dimension of the available databases. Clustering strategies are employed in various sectors like information retrieval, social network analytics, image processing, and so on. Clustering assists the user to understand dissimilarity and similarity among objects. The big data concept has collected incredible attention from various research areas, like government and industry within a short lifetime. In this paper, a Deep Fractional Calculus-Improved Invasive Weed Op

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50

Liu, Xin, Jiang Wu, Chen Yang, and Wenjun Jiang. "A Maximal Tail Dependence-Based Clustering Procedure for Financial Time Series and Its Applications in Portfolio Selection." Risks 6, no. 4 (2018): 115. http://dx.doi.org/10.3390/risks6040115.

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In this paper, we propose a clustering procedure of financial time series according to the coefficient of weak lower-tail maximal dependence (WLTMD). Due to the potential asymmetry of the matrix of WLTMD coefficients, the clustering procedure is based on a generalized weighted cuts method instead of the dissimilarity-based methods. The performance of the new clustering procedure is evaluated by simulation studies. Finally, we illustrate that the optimal mean-variance portfolio constructed based on the resulting clusters manages to reduce the risk of simultaneous large losses effectively.

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