## Node Similarities from Spreading Activation

2012
##### Project
BISON, RTD Forschungsprojekt
##### Publication type
Contribution to a collection
##### Published in
Bisociative Knowledge Discovery / Berthold, Michael R. (ed.). - Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. - (Lecture Notes in Computer Science ; 7250). - pp. 246-262. - ISBN 978-3-642-31829-0
##### 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.
##### Subject (DDC)
004 Computer Science
##### Cite This
ISO 690THIEL, Kilian, Michael R. BERTHOLD, 2012. Node Similarities from Spreading Activation. In: BERTHOLD, Michael R., ed.. Bisociative Knowledge Discovery. Berlin, Heidelberg:Springer Berlin Heidelberg, pp. 246-262. ISBN 978-3-642-31829-0. Available under: doi: 10.1007/978-3-642-31830-6_17
BibTex
@incollection{Thiel2012Simil-19474,
year={2012},
doi={10.1007/978-3-642-31830-6_17},
number={7250},
isbn={978-3-642-31829-0},
publisher={Springer Berlin Heidelberg},
series={Lecture Notes in Computer Science},
booktitle={Bisociative Knowledge Discovery},
pages={246--262},
editor={Berthold, Michael R.},
author={Thiel, Kilian and Berthold, Michael R.},
note={Open Access}
}

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<dcterms:bibliographicCitation>Bisociative Knowledge Discovery : An Introduction to Concept, Algorithms, Tools, and Applications / Michael R. Berthold (ed.). - Heidelberg [u.a.] : Springer, 2012. - S. 246-262. - (Lecture Notes in Computer Science ; 7250 : Lecture notes in artificial intelligence). - ISBN 978-3-642-31829-0</dcterms:bibliographicCitation>
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<dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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Open Access
Yes