Clustering and Closure Coefficient Based on k-CT Components

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 and closure coefficients for evaluation of th...

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Main Authors: Petr Prokop, Vaclav Snasel, Pavla Drazdilova, Jan Platos
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9103498/
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spelling doaj-75137f01eda84528b23a4a5e04640b6c2021-03-30T02:25:26ZengIEEEIEEE Access2169-35362020-01-01810114510115210.1109/ACCESS.2020.29987449103498Clustering and Closure Coefficient Based on k-CT ComponentsPetr Prokop0Vaclav Snasel1Pavla Drazdilova2Jan Platos3https://orcid.org/0000-0002-8481-0136Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, Czech RepublicDepartment of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, Czech RepublicDepartment of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, Czech RepublicDepartment of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, Ostrava, Czech RepublicReal-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 and closure coefficients for evaluation of the network structure. These coefficients are able to describe the inner structure of the network concerning its randomized or organized behavior. Moreover, the coefficients can cluster networks with similar structures together. The experiments show that the coefficients are useful in both the local and global context.https://ieeexplore.ieee.org/document/9103498/Closed trail distanceclustering coefficientclosure coefficientcyclic structurehigher-order structure
collection DOAJ
language English
format Article
sources DOAJ
author Petr Prokop
Vaclav Snasel
Pavla Drazdilova
Jan Platos
spellingShingle Petr Prokop
Vaclav Snasel
Pavla Drazdilova
Jan Platos
Clustering and Closure Coefficient Based on k-CT Components
IEEE Access
Closed trail distance
clustering coefficient
closure coefficient
cyclic structure
higher-order structure
author_facet Petr Prokop
Vaclav Snasel
Pavla Drazdilova
Jan Platos
author_sort Petr Prokop
title Clustering and Closure Coefficient Based on k-CT Components
title_short Clustering and Closure Coefficient Based on k-CT Components
title_full Clustering and Closure Coefficient Based on k-CT Components
title_fullStr Clustering and Closure Coefficient Based on k-CT Components
title_full_unstemmed Clustering and Closure Coefficient Based on k-CT Components
title_sort clustering and closure coefficient based on k-ct components
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description 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 and closure coefficients for evaluation of the network structure. These coefficients are able to describe the inner structure of the network concerning its randomized or organized behavior. Moreover, the coefficients can cluster networks with similar structures together. The experiments show that the coefficients are useful in both the local and global context.
topic Closed trail distance
clustering coefficient
closure coefficient
cyclic structure
higher-order structure
url https://ieeexplore.ieee.org/document/9103498/
work_keys_str_mv AT petrprokop clusteringandclosurecoefficientbasedonkctcomponents
AT vaclavsnasel clusteringandclosurecoefficientbasedonkctcomponents
AT pavladrazdilova clusteringandclosurecoefficientbasedonkctcomponents
AT janplatos clusteringandclosurecoefficientbasedonkctcomponents
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