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- W1994746568 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." @default.
- W1994746568 created "2016-06-24" @default.
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- W1994746568 date "2007-03-01" @default.
- W1994746568 modified "2023-09-28" @default.
- W1994746568 title "Likelihood-based Clustering of Directed Graphs" @default.
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- W1994746568 doi "https://doi.org/10.1109/isciii.2007.367387" @default.
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