Node similarities from spreading activation

Authors:
Kilian Thiel;Michael R. Berthold
Affiliations:
Nycomed Chair for Bioinformatics and Information Mining, Dept. of Computer and Information Science, University of Konstanz, Konstanz, Germany;Nycomed Chair for Bioinformatics and Information Mining, Dept. of Computer and Information Science, University of Konstanz, Konstanz, Germany
Venue:
Bisociative Knowledge Discovery
Year:
2012

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Abstract

In this paper we propose two methods to derive different kinds of node neighborhood based similarities in a network. The first similarity measure focuses on the overlap of direct and indirect neighbors. The second similarity compares nodes based on the structure of their possibly also very distant neighborhoods. Both similarities are derived from spreading activation patterns over time. Whereas in the first method the activation patterns are directly compared, in the second method the relative change of activation over time is compared. We applied both methods to a real world graph dataset and discuss some of the results in more detail.