DocumentCode :
2707334
Title :
Likelihood-based Clustering of Directed Graphs
Author :
Nepusz, Tamás ; Bazsó, óFülöp
Author_Institution :
Budapest Univ. of Technol. & Econ., Budapest
fYear :
2007
fDate :
28-30 March 2007
Firstpage :
189
Lastpage :
194
Abstract :
In this paper, a new, stochastic approach to the clustering of directed graphs is presented. This method differs from the commonly used ones by defining the term "cluster" in an alternative way: a cluster can even be a set of vertices that don\´t connect to each other at all, provided that they have the same connectional preference to other vertices. First, a short overview of the current state of the art will be given. Then the underlying theory of this alternative clustering method will be explained and a possible implementation will be proposed. To support the validity of this approach, benchmark results on computer-generated graphs as well as two real applications are presented.
Keywords :
directed graphs; pattern clustering; stochastic processes; computer-generated graphs; likelihood-based clustering; of irected graphs; stochastic approach; Application software; Clustering methods; Collaborative software; IP networks; Information systems; Nuclear physics; Social network services; Stochastic processes; Symmetric matrices; Web pages;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Intelligent Informatics, 2007. ISCIII '07. International Symposium on
Conference_Location :
Agadir
Print_ISBN :
1-4244-1158-0
Electronic_ISBN :
1-4244-1158-0
Type :
conf
DOI :
10.1109/ISCIII.2007.367387
Filename :
4218420
Link To Document :
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