Results 31 to 40 of about 577,453 (293)
An Improved Link Prediction Approach for Directed Complex Networks Using Stochastic Block Modeling
Big Data and Cognitive Computing, 2023 Link prediction finds the future or the missing links in a social–biological complex network such as a friendship network, citation network, or protein network.
Lekshmi S. Nair +2 more
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IEEE Access, 2020 Graph theory analysis, a mathematical approach, has been applied in brain connectivity studies to explore the organization of network patterns.
Lina Elsherif Ismail, Waldemar Karwowski
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Limit theorems for assortativity and clustering in null models for scale-free networks [PDF]
, 2019 An important problem in modeling networks is how to generate a randomly sampled graph with given degrees. A popular model is the configuration model, a network with assigned degrees and random connections.
Litvak, Nelly +3 more
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Cluster detection of spatial regression coefficients [PDF]
Statistics in Medicine, 2016 Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying‐coefficient regression and spatial clusters in the regression coefficients. For varying‐coefficient regression, such as the geographically weighted regression, different regression coefficients are ...
Lee, Junho, Gangnon, Ronald E., Zhu, Jun
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Clustering and Closure Coefficient Based on k-CT Components
IEEE Access, 2020 Real-world networks contain many cliques since they are usually built from them. The analysis that goes behind the cliques is fundamental because it discovers the real structure of the network. This article proposed new high-order closed trail clustering
Petr Prokop +3 more
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On Learning Cluster Coefficient of Private Networks [PDF]
2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012 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 tabular data.
Yue, Wang +3 more
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Simulating retrieval from a highly clustered network: Implications for spoken word recognition
Frontiers in Psychology, 2011 Network science describes how entities in complex systems interact, and argues that the structure of the network influences processing. Clustering coefficient, C—one measure of network structure—refers to the extent to which neighbors of a node are also ...
Michael S Vitevitch +2 more
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Approximating Clustering Coefficient and Transitivity
Journal of Graph Algorithms and Applications, 2005 Summary: 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 complex systems are huge, the efficient computation
Schank, Thomas, Wagner, Dorothea
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Clustering coefficients of protein-protein interaction networks [PDF]
Physical Review E, 2007 16 pages, 3 figures, in Press PRE uses ...
Miller, Gerald A. +3 more
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Systemic risk assessment through high order clustering coefficient
, 2020 In this article we propose a novel measure of systemic risk in the context of financial networks. To this aim, we provide a definition of systemic risk which is based on the structure, developed at different levels, of clustered neighbours around the ...
Cerqueti, Roy +2 more
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