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The Clustering Coefficient and the

by Yunlin Zhang, Mingliang Liu, Mark A. Van Dijk, Guangwei Zhu, Zhijun Gong, Yunliang Li - Diameter of Small-world Networks, Acta Mathematica Sinca, English Series,2013,29(1):199-208
"... Measured and numerically partitioned phytoplankton spectral absorption ..."
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Measured and numerically partitioned phytoplankton spectral absorption

clustering coefficient

by Liudmila Ostroumova, Er Ryabchenko, Egor Samosvat
"... tunable power-law degree distribution and ..."
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tunable power-law degree distribution and

Approximating clustering coefficient and transitivity

by Thomas Schank, Dorothea Wagner - Journal of Graph Algorithms and Applications , 2005
"... Since its introduction in the year 1998 by Watts and Strogatz, the clustering coefficient has become a frequently used tool for analyzing graphs. In 2002 the transitivity was proposed by Newman, Watts and Strogatz as an alternative to the clustering coefficient. As many networks considered in comple ..."
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Since its introduction in the year 1998 by Watts and Strogatz, the clustering coefficient has become a frequently used tool for analyzing graphs. In 2002 the transitivity was proposed by Newman, Watts and Strogatz as an alternative to the clustering coefficient. As many networks considered

Clustering coefficient for weighted networks

by Gabriela Kalna, Desmond J. Higham , 2006
"... The clustering coefficient has been used successfully to summarise important features of unweighted, undirected networks across a wide range of applications. Recently, a number of authors have extended this concept to the case of networks with non-negatively weighted edges. After reviewing various a ..."
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The clustering coefficient has been used successfully to summarise important features of unweighted, undirected networks across a wide range of applications. Recently, a number of authors have extended this concept to the case of networks with non-negatively weighted edges. After reviewing various

EXTENDED CLUSTERING COEFFICIENTS:GENERALIZATION OF CLUSTERING COEFFICIENTS IN SMALL-WORLD NETWORKS ∗

by Wenjun Xiao, Wenhong Wei, Weidong Chen, Yong Qin, Behrooz Parhami
"... The clustering coefficient C of a network, which is a measure of direct connectivity between neighbors of the various nodes, ranges from 0 (for no connectivity) to 1 (for full connectivity). We define extended clustering coefficients C(h) of a small-world network based on nodes that are at distance ..."
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The clustering coefficient C of a network, which is a measure of direct connectivity between neighbors of the various nodes, ranges from 0 (for no connectivity) to 1 (for full connectivity). We define extended clustering coefficients C(h) of a small-world network based on nodes that are at distance

On Learning Cluster Coefficient of Private Networks

by Yue Wang, Xintao Wu, Jun Zhu, Yang Xiang
"... Abstract—Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum) on tabula ..."
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Abstract—Enabling accurate analysis of social network data while preserving differential privacy has been challenging since graph features such as clustering coefficient or modularity often have high sensitivity, which is different from traditional aggregate functions (e.g., count and sum

Computing Clustering Coefficients in Data Streams

by Lucian Salete Buriol , Gereon Frahling, Stefano Leonardi, Alberto Marchetti-spaccamela, Christian Sohler
"... We present random sampling algorithms that with probability at least 1 − δ compute a (1 ± ǫ)approximation of the clustering coefficient, the transitivity coefficient, and of the number of bipartite cliques in a graph given as a stream of edges. Our methods can be extended to approximately count the ..."
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We present random sampling algorithms that with probability at least 1 − δ compute a (1 ± ǫ)approximation of the clustering coefficient, the transitivity coefficient, and of the number of bipartite cliques in a graph given as a stream of edges. Our methods can be extended to approximately count

Global clustering coefficient in scale-free networks

by Liudmila Ostroumova Prokhorenkova, Egor Samosvat
"... Abstract. In this paper, we analyze the behavior of the global cluster-ing coefficient in scale free graphs. We are especially interested in the case of degree distribution with an infinite variance, since such degree distribution is usually observed in real-world networks of diverse nature. There a ..."
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Abstract. In this paper, we analyze the behavior of the global cluster-ing coefficient in scale free graphs. We are especially interested in the case of degree distribution with an infinite variance, since such degree distribution is usually observed in real-world networks of diverse nature

Weighted Clustering Coefficient Maximization For Air Transportation Networks

by Julien Ponton, Peng Wei, Dengfeng Sun
"... Abstract — In transportation networks the robustness of a net-work regarding nodes and links failures is a key factor for its design. At the same time, traveling passengers usually prefer the itinerary with fewer legs. The average clustering coefficient can be used to measure the robustness of a net ..."
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Abstract — In transportation networks the robustness of a net-work regarding nodes and links failures is a key factor for its design. At the same time, traveling passengers usually prefer the itinerary with fewer legs. The average clustering coefficient can be used to measure the robustness of a

On the Streaming Complexity of Computing Local Clustering Coefficients

by Konstantin Kutzkov, Rasmus Pagh , 2013
"... Due to a large number of applications, the problem of estimating the number of triangles in graphs revealed as a stream of edges, and the closely related problem of estimating the graph’s clustering coefficient, have received considerable attention in the last decade. Both efficient algorithms and i ..."
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Due to a large number of applications, the problem of estimating the number of triangles in graphs revealed as a stream of edges, and the closely related problem of estimating the graph’s clustering coefficient, have received considerable attention in the last decade. Both efficient algorithms
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