z-logo
open-access-imgOpen Access
Network Clustering Algorithm Based on Fast Detection of Central Node
Author(s) -
Ziruo Jia,
Fuqiang Qi
Publication year - 2022
Publication title -
scientific programming
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.269
H-Index - 36
eISSN - 1875-919X
pISSN - 1058-9244
DOI - 10.1155/2022/4905190
Subject(s) - cluster analysis , computer science , correlation clustering , cure data clustering algorithm , canopy clustering algorithm , data stream clustering , determining the number of clusters in a data set , single linkage clustering , node (physics) , data mining , algorithm , fuzzy clustering , pattern recognition (psychology) , artificial intelligence , engineering , structural engineering
Based on scale-free and density-based complex networks and numerical clustering algorithm, a graph clustering algorithm based on fast detection of central nodes is proposed. Through the calculation of local density and comprehensive clustering of nodes in the network, the clustering center in the network can be found quickly and noncentral nodes can be divided into the clustering center according to the nearest neighbor principle, thus avoiding parameter limitations such as the number of clustering to be set in advance using conventional classic social network detection algorithm. The experimental comparison and analysis in the real network indicate that the graph clustering algorithm based on fast detection of the central node is highly effective and efficient.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom