Title :
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
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;
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
DOI :
10.1109/ASONAM.2009.74
