Social Network Community Detection Using Agglomerative Spectral Clustering

Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and...

Full description

Saved in:
Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2017-01, Vol.2017 (2017), p.1-10
Main Authors: Narantsatsralt, Ulzii-Utas, Kang, Sanggil
Format: Article
Language:English
Subjects:
Accuracy
Agglomeration
Algorithms
Analysis
Cluster analysis
Clustering
Communities
Computer engineering
Computer science
Data mining
Eigenvectors
International conferences
Methods
Networks
Neural networks
Resistance
Similarity measures
Social networks
Spectra
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Community detection has become an increasingly popular tool for analyzing and researching complex networks. Many methods have been proposed for accurate community detection, and one of them is spectral clustering. Most spectral clustering algorithms have been implemented on artificial networks, and accuracy of the community detection is still unsatisfactory. Therefore, this paper proposes an agglomerative spectral clustering method with conductance and edge weights. In this method, the most similar nodes are agglomerated based on eigenvector space and edge weights. In addition, the conductance is used to identify densely connected clusters while agglomerating. The proposed method shows improved performance in related works and proves to be efficient for real life complex networks from experiments.
ISSN:1076-2787
1099-0526
DOI:10.1155/2017/3719428