Paper Info
Title
Structure and attributes community detection: comparative analysis of composite, ensemble and selection methods
Abstract
In recent years due to the rise of social, biological, and other rich content graphs, several new graph clustering methods using structure and node's attributes have been introduced. In this paper, we compare our novel clustering method, termed Selection method, against seven clustering methods: three structure and attribute methods, one structure only method, one attribute only method, and two ensemble methods. The Selection method uses the graph structure ambiguity to switch between structure and attribute clustering methods. We shows that the Selection method out performed the state-of-art structure and attribute methods.
Year
DOI
Venue
2013
10.1145/2501025.2501034
SNAKDD
Keywords
Field
DocType
graph structure ambiguity,ensemble method,state-of-art structure,attribute method,comparative analysis,clustering method,rich content graph,new graph,selection method,recent year,community detection,social network analysis
Data mining,Graph,Correlation clustering,Pattern recognition,Computer science,Social network analysis,Artificial intelligence,Cluster analysis,Clustering coefficient,Ambiguity,Ensemble learning,Single-linkage clustering
Conference
Citations 
PageRank 
References 
5
0.46
12
Authors
2
Name
Order
Citations
PageRank
Haithum Elhadi1111.76
Gady Agam239143.99