Relevant bibliographies by topics / Local clustering coefficient

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Local clustering coefficient'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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Journal articles on the topic "Local clustering coefficient"

1

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.

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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 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.
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Meghanathan, Natarajan. "Local clustering coefficient-based assortativity analysis of real-world network graphs." International Journal of Network Science 1, no. 3 (2017): 187. http://dx.doi.org/10.1504/ijns.2017.083577.

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Meghanathan, Natarajan. "Local clustering coefficient-based assortativity analysis of real-world network graphs." International Journal of Network Science 1, no. 3 (2017): 187. http://dx.doi.org/10.1504/ijns.2017.10004296.

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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 (May 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 clustering coefficient and the standard deviation of clustering coefficient decrease with the user reputation, which are different from the results that the average clustering coefficient and the standard deviation of clustering coefficient remain stable regardless of user reputation in the null model, suggesting that the user interest tends to be multiple and the diversity of the user interests is centralized for users with high reputation. Furthermore, we divide users into seven groups according to the user degree and investigate the heterogeneity of rating behavior patterns. The results show that the relation of user reputation and clustering coefficient is obvious for small degree users and weak for large degree users, reflecting an important connection between user degree and collective rating behavior patterns. This work provides a further understanding on the intrinsic association between user collective behaviors and user reputation.
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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 (September 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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Yang, Chun-Xia, Min-Xuan Tang, Hai-Qiang Tang, and Qiang-Qiang Deng. "Local-world and cluster-growing weighted networks with controllable clustering." International Journal of Modern Physics C 25, no. 05 (March 11, 2014): 1440009. http://dx.doi.org/10.1142/s0129183114400099.

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We constructed an improved weighted network model by introducing local-world selection mechanism and triangle coupling mechanism based on the traditional BBV model. The model gives power-law distributions of degree, strength and edge weight and presents the linear relationship both between the degree and strength and between the degree and the clustering coefficient. Particularly, the model is equipped with an ability to accelerate the speed increase of strength exceeding that of degree. Besides, the model is more sound and efficient in tuning clustering coefficient than the original BBV model. Finally, based on our improved model, we analyze the virus spread process and find that reducing the size of local-world has a great inhibited effect on virus spread.
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Wang, Yu, Eshwar Ghumare, Rik Vandenberghe, and Patrick Dupont. "Comparison of Different Generalizations of Clustering Coefficient and Local Efficiency for Weighted Undirected Graphs." Neural Computation 29, no. 2 (February 2017): 313–31. http://dx.doi.org/10.1162/neco_a_00914.

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Binary undirected graphs are well established, but when these graphs are constructed, often a threshold is applied to a parameter describing the connection between two nodes. Therefore, the use of weighted graphs is more appropriate. In this work, we focus on weighted undirected graphs. This implies that we have to incorporate edge weights in the graph measures, which require generalizations of common graph metrics. After reviewing existing generalizations of the clustering coefficient and the local efficiency, we proposed new generalizations for these graph measures. To be able to compare different generalizations, a number of essential and useful properties were defined that ideally should be satisfied. We applied the generalizations to two real-world networks of different sizes. As a result, we found that not all existing generalizations satisfy all essential properties. Furthermore, we determined the best generalization for the clustering coefficient and local efficiency based on their properties and the performance when applied to two networks. We found that the best generalization of the clustering coefficient is [Formula: see text], defined in Miyajima and Sakuragawa ( 2014 ), while the best generalization of the local efficiency is [Formula: see text], proposed in this letter. Depending on the application and the relative importance of sensitivity and robustness to noise, other generalizations may be selected on the basis of the properties investigated in this letter.
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Liu, Saisai, and Zhengyou Xia. "A two-stage BFS local community detection algorithm based on node transfer similarity and Local Clustering Coefficient." Physica A: Statistical Mechanics and its Applications 537 (January 2020): 122717. http://dx.doi.org/10.1016/j.physa.2019.122717.

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GRABOWSKI, ANDRZEJ, and ROBERT A. KOSIŃSKI. "PROPERTIES OF AN EVOLVING DIRECTED NETWORK WITH LOCAL RULES AND INTRINSIC VARIABLES." International Journal of Modern Physics C 18, no. 01 (January 2007): 43–52. http://dx.doi.org/10.1142/s0129183107010243.

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We present a simple model of an evolving directed network based on local rules. It leads to a complex network with the properties of real systems, like scale-free distribution of outgoing and incoming connectivity, and a hierarchical structure. Each node is characterised by an intrinsic variable S, and the number of outgoing links k out . As a result of network evolution the number of nodes and links (as well as their location) changes in time. For critical values of control parameters there is a transition to a scale-free network. Results for connectivity distribution found analytically agree with numerical calculations. Our model also reproduces other nontrivial properties of real networks, e.g. a large clustering coefficient and weak correlations between the age of a node and its connectivity. We have discovered an unexpected phenomenon that noise can increase the value of the clustering coefficient, whose large value is characteristic for a regular network.
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Li, Xin Ye. "XML Document Clustering Based on Spectral Analysis Method." Advanced Materials Research 219-220 (March 2011): 304–7. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.304.

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While K-Means algorithm usually gets local optimal solution, spectral clustering method can obtain satisfying clustering results through embedding the data points into a new space in which clusters are tighter. Since traditional spectral clustering method uses Gauss Kernel Function to compute the similarity between two points, the selection of scale parameter σ is related with domain knowledge usually. This paper uses spectral method to cluster XML documents. To consider both element and structure of XML documents, this paper proposes to use path feature to represent XML document; to avoild the selection of scale parameter σ, it also proposes to use Jaccard coefficient to compute the similarity between two XML documents. Experiment shows that using Jaccard coefficient to compute the similarity is effective, the clustering result is correct.
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Dissertations / Theses on the topic "Local clustering coefficient"

1

Shiping, Liu. "Synthetic notions of curvature and applications in graph theory." Doctoral thesis, Universitätsbibliothek Leipzig, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-102197.

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The interaction between the study of geometric and analytic aspects of Riemannian manifolds and that of graphs is a very amazing subject. The study of synthetic curvature notions on graphs adds new contributions to this topic. In this thesis, we mainly study two kinds of synthetic curvature notions: the Ollivier-Ricci cuvature on locally finite graphs and the combinatorial curvature on infinite semiplanar graphs. In the first part, we study the Ollivier-Ricci curvature. As known in Riemannian geometry, a lower Ricci curvature bound prevents geodesics from diverging too fast on average. We translate this Riemannian idea into a combinatorial setting using the Olliver-Ricci curvature notion. Note that on a graph, the analogue of geodesics starting in different directions, but eventually approaching each other again, would be a triangle. We derive lower and upper Ollivier-Ricci curvature bounds on graphs in terms of number of triangles, which is sharp for instance for complete graphs. We then describe the relation between Ollivier-Ricci curvature and the local clustering coefficient, which is an important concept in network analysis introduced by Watts-Strogatz. Furthermore, positive lower boundedness of Ollivier-Ricci curvature for neighboring vertices imply the existence of at least one triangle. It turns out that the existence of triangles can also improve Lin-Yau\'s curvature dimension inequality on graphs and then produce an implication from Ollivier-Ricci curvature lower boundedness to the curvature dimension inequality. The existence of triangles prevents a graph from being bipartite. A finite graph is bipartite if and only if its largest eigenvalue equals 2. Therefore it is natural that Ollivier-Ricci curvature is closely related to the largest eigenvalue estimates. We combine Ollivier-Ricci curvature notion with the neighborhood graph method developed by Bauer-Jost to study the spectrum estimates of a finite graph. We can always obtain nontrivial estimates on a non-bipartite graph even if its curvature is nonpositive. This answers one of Ollivier\'s open problem in the finite graph setting. In the second part of this thesis, we study systematically infinite semiplanar graphs with nonnegative combinatorial curvature. Unlike the previous Gauss-Bonnet formula approach, we explore an Alexandrov approach based on the observation that the nonnegative combinatorial curvature on a semiplanar graph is equivalent to nonnegative Alexandrov curvature on the surface obtained by replacing each face by a regular polygon of side length one with the same facial degree and gluing the polygons along common edges. Applying Cheeger-Gromoll splitting theorem on the surface, we give a metric classification of infinite semiplanar graphs with nonnegative curvature. We also construct the graphs embedded into the projective plane minus one point. Those constructions answer a question proposed by Chen. We further prove the volume doubling property and Poincare inequality which make the running of Nash-Moser iteration possible. We in particular explore the volume growth behavior on Archimedean tilings on a plane and prove that they satisfy a weak version of relative volume comparison with constant 1. With the above two basic inequalities in hand, we study the geometric function theory of infinite semiplanar graphs with nonnegative curvature. We obtain the Liouville type theorem for positive harmonic functions, the parabolicity. We also prove a dimension estimate for polynomial growth harmonic functions, which is an extension of the solution of Colding-Minicozzi of a conjecture of Yau in Riemannian geometry.
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Yao, Chen-Han, and 姚成翰. "Reliable Local Recovery Routing Protocol with Clustering Coefficient for Ad Hoc Networks." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/21964992404323705393.

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博士
淡江大學
資訊工程學系博士班
100
Nodes in mobile ad hoc network communicate with each other through wireless multi-hop links. When a node wants to send data to another node, it uses some routing protocol to find the path. In on-demand routing protocols, the source starts a route discovery to find the route leading to the destination. Route discovery is typically performed via flooding, which consumes a lot of control packets. Because of node mobility, the network topology change frequently and cause the route broken. Traditional routing protocols restart a route discovery when link failure. In this thesis, we propose two on-demand local recovery routing protocols based on clustering coefficient, (I) "Local Path Recovery Routing Protocol based on Clustering Coefficient "(LPRCC), (II) "Reliable Local Recovery Routing Protocol based on Clustering Coefficient"(RLRCC). Our first protocol LPRCC use route clustering coefficient to choose routing path. When link failure occurs, nodes can quickly salvage the data without starting another route discovery. Our second protocol RLRCC choose a route with higher route score, route score is calculated by link stable value and node triangle value. RLRCC can decrease the number of route failure occur and also can reduce the route discovery times. Simulation results show both of our protocols can decrease the number of control packets and increase route delivery ratio.
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Tsai, Kai-Siang, and 蔡凱翔. "Using local link switching algorithm to control directed and weight network clustering coefficient." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/94797731947635120347.

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碩士
淡江大學
資訊工程學系碩士班
98
Over the past decade the studies of complex networks have been analyzed and researched. In analyzing Clustering coefficient is a important concept Clustering coefficient characterizes the relative tightness of a network and is a defining network statistics that appears in many “real-world” network data. This paper proposed a local link switching algorithm which effectively increases the clustering coefficient of a directed weight network while preserving the network node degree distributions. This link switching algorithm is based on local neighborhood information. Link switching algorithm is widely used in producing similar networks with the same degree distribution, that is, it is used in ‘sampling’ networks from the same network pool. How to use this algorithm to implement in directed and weight network is major study in this paper.
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Lee, Che-Chun, and 李哲均. "Finding Overlapping Communities by Local Clustering Coefficients of Seed Nodes." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/m37pq3.

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Huang, Shi-Yu, and 黃士育. "Overlapping Community Discovery by Combining Local Clustering Coefficients and Neighbor Relationship Measurements." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/r4tqjq.

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碩士
樹德科技大學
資訊工程系碩士班
105
Most users of online social networks play different roles at different times due to the diversity of their interests. Overlapping community discovery studies the complexity involved in interpersonal social networks, using various techniques of Social Network Analysis (SNA). SNA identifies seed nodes of social networks, based on which hidden overlapping communities could be found by gradually merging neighboring seeds to form large groups. In methods that select nodes of high degrees only, close-knit groups consisting of nodes of low degrees are often neglected. To overcome the problem, this study proposes to select nodes of high Local Clustering Coefficients (LCC) as seeds and then examine the relationship degrees between neighboring seeds to discover overlapping communities. The proposed method was compared with those adopting nodes of high degrees as seeds, as well as the famous Clique Percolation Method (CPM). The result showed effective improvement in grouping quality and graph efficiency.
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6

Shiping, Liu. "Synthetic notions of curvature and applications in graph theory." Doctoral thesis, 2012. https://ul.qucosa.de/id/qucosa%3A11816.

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The interaction between the study of geometric and analytic aspects of Riemannian manifolds and that of graphs is a very amazing subject. The study of synthetic curvature notions on graphs adds new contributions to this topic. In this thesis, we mainly study two kinds of synthetic curvature notions: the Ollivier-Ricci cuvature on locally finite graphs and the combinatorial curvature on infinite semiplanar graphs. In the first part, we study the Ollivier-Ricci curvature. As known in Riemannian geometry, a lower Ricci curvature bound prevents geodesics from diverging too fast on average. We translate this Riemannian idea into a combinatorial setting using the Olliver-Ricci curvature notion. Note that on a graph, the analogue of geodesics starting in different directions, but eventually approaching each other again, would be a triangle. We derive lower and upper Ollivier-Ricci curvature bounds on graphs in terms of number of triangles, which is sharp for instance for complete graphs. We then describe the relation between Ollivier-Ricci curvature and the local clustering coefficient, which is an important concept in network analysis introduced by Watts-Strogatz. Furthermore, positive lower boundedness of Ollivier-Ricci curvature for neighboring vertices imply the existence of at least one triangle. It turns out that the existence of triangles can also improve Lin-Yau\''s curvature dimension inequality on graphs and then produce an implication from Ollivier-Ricci curvature lower boundedness to the curvature dimension inequality. The existence of triangles prevents a graph from being bipartite. A finite graph is bipartite if and only if its largest eigenvalue equals 2. Therefore it is natural that Ollivier-Ricci curvature is closely related to the largest eigenvalue estimates. We combine Ollivier-Ricci curvature notion with the neighborhood graph method developed by Bauer-Jost to study the spectrum estimates of a finite graph. We can always obtain nontrivial estimates on a non-bipartite graph even if its curvature is nonpositive. This answers one of Ollivier\''s open problem in the finite graph setting. In the second part of this thesis, we study systematically infinite semiplanar graphs with nonnegative combinatorial curvature. Unlike the previous Gauss-Bonnet formula approach, we explore an Alexandrov approach based on the observation that the nonnegative combinatorial curvature on a semiplanar graph is equivalent to nonnegative Alexandrov curvature on the surface obtained by replacing each face by a regular polygon of side length one with the same facial degree and gluing the polygons along common edges. Applying Cheeger-Gromoll splitting theorem on the surface, we give a metric classification of infinite semiplanar graphs with nonnegative curvature. We also construct the graphs embedded into the projective plane minus one point. Those constructions answer a question proposed by Chen. We further prove the volume doubling property and Poincare inequality which make the running of Nash-Moser iteration possible. We in particular explore the volume growth behavior on Archimedean tilings on a plane and prove that they satisfy a weak version of relative volume comparison with constant 1. With the above two basic inequalities in hand, we study the geometric function theory of infinite semiplanar graphs with nonnegative curvature. We obtain the Liouville type theorem for positive harmonic functions, the parabolicity. We also prove a dimension estimate for polynomial growth harmonic functions, which is an extension of the solution of Colding-Minicozzi of a conjecture of Yau in Riemannian geometry.
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Book chapters on the topic "Local clustering coefficient"

1

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.

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Zhang, Hao, Yuanyuan Zhu, Lu Qin, Hong Cheng, and Jeffrey Xu Yu. "Efficient Local Clustering Coefficient Estimation in Massive Graphs." In Database Systems for Advanced Applications, 371–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55699-4_23.

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Liu, Zichun, Hongli Xu, Liusheng Huang, and Wei Yang. "Estimating Clustering Coefficient of Multiplex Graphs with Local Differential Privacy." In Wireless Algorithms, Systems, and Applications, 390–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86137-7_42.

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Jiang, Xiaoliang, Dongsong Zhang, Huan Lin, Xin Li, Junjian Xiao, and Bailin Li. "A Robust Image Segmentation Approach Using Fuzzy C-Means Clustering with Local Coefficient of Variation." In Advances in Intelligent Systems and Computing, 93–102. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8944-2_12.

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Su, Yi-Jen, and Che-Chun Lee. "Overlapping Community Detection with Two-Level Expansion by Local Clustering Coefficients." In Security with Intelligent Computing and Big-data Services, 105–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76451-1_11.

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"Watts-Strogatz Local Clustering Coefficient." In Encyclopedia of Systems Biology, 2350. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_101627.

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"Centrality Metrics, Measures, and Real-World Network Graphs." In Advances in Wireless Technologies and Telecommunication, 1–33. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3802-8.ch001.

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This chapter provides an introduction to various node and edge centrality metrics that are studied throughout this book. The authors describe the procedure to compute these metrics and illustrate the same with an example. The node centrality metrics described are degree centrality (DEG), eigenvector centrality (EVC), betweenness centrality (BWC), closeness centrality (CLC), and the local clustering coefficient complement-based degree centrality (LCC'DC). The edge centrality metrics described are edge betweenness centrality (EBWC) and neighborhood overlap (NOVER). The authors then describe the three different correlation measures—Pearson's, Spearman's, and Kendall's measures—that are used in this book to analyze the correlation between any two centrality metrics. Finally, the authors provide a brief description of the 50 real-world network graphs that are studied in some of the chapters of this book.
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"Computationally Light vs. Computationally Heavy Centrality Metrics." In Advances in Wireless Technologies and Telecommunication, 34–65. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3802-8.ch002.

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In this chapter, the authors analyze the correlation between the computationally light degree centrality (DEG) and local clustering coefficient complement-based degree centrality (LCC'DC) metrics vs. the computationally heavy betweenness centrality (BWC), eigenvector centrality (EVC), and closeness centrality (CLC) metrics. Likewise, they also analyze the correlation between the computationally light complement of neighborhood overlap (NOVER') and the computationally heavy edge betweenness centrality (EBWC) metric. The authors analyze the correlations at three different levels: pair-wise (Kendall's correlation measure), network-wide (Spearman's correlation measure), and linear regression-based prediction (Pearson's correlation measure). With regards to the node centrality metrics, they observe LCC'DC-BWC to be the most strongly correlated at all the three levels of correlation. For the edge centrality metrics, the authors observe EBWC-NOVER' to be strongly correlated with respect to the Spearman's correlation measure, but not with respect to the other two measures.
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Meghanathan, Natarajan, Md Atiqur Rahman, and Mahzabin Akhter. "Centrality Metrics-Based Connected Dominating Sets for Real-World Network Graphs." In Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking, 1–29. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8188-8.ch001.

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The authors investigate the use of centrality metrics as node weights to determine connected dominating sets (CDS) for a suite of 60 real-world network graphs of diverse degree distribution. They employ centrality metrics that are neighborhood-based (degree centrality [DEG] and eigenvector centrality [EVC]), shortest path-based (betweenness centrality [BWC] and closeness centrality [CLC]) as well as the local clustering coefficient complement-based degree centrality metric (LCC'DC), which is a hybrid of the neighborhood and shortest path-based categories. The authors target for minimum CDS node size (number of nodes constituting the CDS). Though both the BWC and CLC are shortest path-based centrality metrics, they observe the BWC-based CDSs to be of the smallest node size for about 60% of the real-world networks and the CLC-based CDSs to be of the largest node size for more than 40% of the real-world networks. The authors observe the computationally light LCC'DC-based CDS node size to be the same as the computationally heavy BWC-based CDS node size for about 50% of the real-world networks.
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Latora, Vito, and Massimo Marchiori. "The Architecture of Complex Systems." In Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.003.0027.

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At the present time, the most commonly accepted definition of a complex system is that of a system containing many interdependent constituents which interact nonlinearly. Therefore, when we want to model a complex system, the first issue has to do with the connectivity properties of its network, the architecture of the wirings between the constituents. In fact, we have recently learned that the network structure can be as important as the nonlinear interactions between elements, and an accurate description of the coupling architecture and a characterization of the structural properties of the network can be of fundamental importance also in understanding the dynamics of the system. In the last few years the research on networks has taken different directions producing rather unexpected and important results. Researchers have: (1) proposed various global variables to describe and characterize the properties of realworld networks and (2) developed different models to simulate the formation and the growth of networks such as the ones found in the real world. The results obtained can be summed up by saying that statistical physics has been able to capture the structure of many diverse systems within a few common frameworks, though these common frameworks are very different from the regular array, or capture the random connectivity, previously used to model the network of a complex system. Here we present a list of some of the global quantities introduced to characterize a network: the characteristic path length L, the clustering coefficient C, the global efficiency E<sub>glob</sub>, the local efficiency E<sub>loc</sub>, the cost Cost, and the degree distribution P(k). We also review two classes of networks proposed: smallworld and scale-free networks. We conclude with a possible application of the nonextensive thermodynamics formalism to describe scale-free networks. Watts and Strogatz [17] have shown that the connection topology of some biological, social, and technological networks is neither completely regular nor completely random. These networks, that are somehow in between regular and random networks, have been named small worlds in analogy with the smallworld phenomenon empirically observed in social systems more than 30 years ago [11, 12].
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Conference papers on the topic "Local clustering coefficient"

1

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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Asmi, Khawla, Dounia Lotfi, and Mohamed El marraki. "A new local algorithm for overlapping community detection based on clustering coefficient and common neighbor similarity." In the ArabWIC 6th Annual International Conference Research Track. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3333165.3333172.

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Kutzkov, Konstantin, and Rasmus Pagh. "On the streaming complexity of computing local clustering coefficients." In the sixth ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2433396.2433480.

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