The clustering coefficient has two definitions that dominate its use, one by Watts and Strogatz, and the second by Newman. It is critical to identify and report the analytic similarities and differences between the two methods in order to give future researchers guidance on the appropriate method to use in their research. This paper reports on an analytical comparison between the two clustering coefficient definitions. We performed the comparison using analytical derivations to show the mathematical relations between the two definitions, the limits on the analysis, and the impact clustering coefficient values derived from the two definitions
Clustering is a central concept in network theory. Nevertheless, its usual formulation as the cluste...
This paper deals with several problems in cluster analysis. It appears that the suggested solutions ...
Three cluster analysis programs were used to group the same 64 individuals, generated so as to repre...
The clustering coefficient has two definitions that dominate its use, one by Watts and Strogatz, and...
Copyright © 2014 Chao Tong et al. This is an open access article distributed under the Creative Comm...
<p>represents the global clustering coefficient; , the mean local clustering coefficient; r, degree ...
<p>For each network, we show its name and reference; number of nodes () and edges (); mean degree ()...
Understanding differences in the results obtained from different article-level clustering approaches...
The brain is undoubtedly the most complex system known to humanity. Networks constitute the backbone...
This paper deals with the question whether the quality of different clustering algorithms can be com...
Clustering has now become a very important tool to manage the data in many areas such as pattern rec...
Poster presentation given at ISSI 2019 conference and the related paper. In these, we describe our ...
Clustering analysis is a crucial part of pattern recognition, which refers to the procedure of patte...
A collection of data is gathered from surveys held in a Spring Course of the Economic Faculty in the...
The article looks at the relation of the clustering coefficient and degree in the network of interlo...
Clustering is a central concept in network theory. Nevertheless, its usual formulation as the cluste...
This paper deals with several problems in cluster analysis. It appears that the suggested solutions ...
Three cluster analysis programs were used to group the same 64 individuals, generated so as to repre...
The clustering coefficient has two definitions that dominate its use, one by Watts and Strogatz, and...
Copyright © 2014 Chao Tong et al. This is an open access article distributed under the Creative Comm...
<p>represents the global clustering coefficient; , the mean local clustering coefficient; r, degree ...
<p>For each network, we show its name and reference; number of nodes () and edges (); mean degree ()...
Understanding differences in the results obtained from different article-level clustering approaches...
The brain is undoubtedly the most complex system known to humanity. Networks constitute the backbone...
This paper deals with the question whether the quality of different clustering algorithms can be com...
Clustering has now become a very important tool to manage the data in many areas such as pattern rec...
Poster presentation given at ISSI 2019 conference and the related paper. In these, we describe our ...
Clustering analysis is a crucial part of pattern recognition, which refers to the procedure of patte...
A collection of data is gathered from surveys held in a Spring Course of the Economic Faculty in the...
The article looks at the relation of the clustering coefficient and degree in the network of interlo...
Clustering is a central concept in network theory. Nevertheless, its usual formulation as the cluste...
This paper deals with several problems in cluster analysis. It appears that the suggested solutions ...
Three cluster analysis programs were used to group the same 64 individuals, generated so as to repre...