AHSCAN: Agglomerative Hierarchical Structural Clustering Algorithm for Networks

Author

Yuruk, Nurcan ; Mete, Mutlu ; Xu, Xiaowei ; Schweiger, Thomas A J

Author_Institution

Appl. Sci., Univ. of Arkansas at Little Rock, Little Rock, AR, USA

fYear

2009

fDate

20-22 July 2009

Firstpage

72

Lastpage

77

Abstract

Many systems in sciences, engineering and nature can be modeled as networks. Examples include the Internet, WWW and social networks. Finding hidden structures is important for making sense of complex networked data. In this paper we present a new network clustering method that can find clusters in an agglomerative fashion using structural similarity of vertices in the given network. Experiments conducted on real datasets demonstrate promising performance of the new method.

Keywords

Internet; pattern clustering; social networking (online); Internet; World Wide Web; agglomerative hierarchical structural clustering; network clustering method; social network; Algorithm design and analysis; Clustering algorithms; IP networks; Information analysis; Information science; Information technology; Iterative algorithms; Partitioning algorithms; Social network services; Systems engineering and theory; Community Structures; Hierarchical Clustering Algorithms; Social Networks; Structual Clustering Algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Social Network Analysis and Mining, 2009. ASONAM '09. International Conference on Advances in

Conference_Location

Athens

Print_ISBN

978-0-7695-3689-7

Type

conf

DOI

10.1109/ASONAM.2009.74

Filename

5231935