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GRAFFITI: Node Labeling in Heterogeneous Networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44021

Angelova,  Ralitsa
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44738

Kasneci,  Gjergji
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45572

Suchanek,  Fabian M.
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Zitation

Angelova, R., Kasneci, G., Suchanek, F. M., & Weikum, G. (2009). GRAFFITI: Node Labeling in Heterogeneous Networks. In Proceedings of the 18th International World Wide Web Conference (pp. 1087-1088). New York, NY: ACM. doi:10.1145/1526709.1526869.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000F-191B-9
Zusammenfassung
We introduce a multi-label classification model and algorithm for labeling heterogeneous networks, where nodes belong to different types and different types have different sets of classification labels. We present a graph-based approach which models the mutual influence between nodes in the network as a random walk. When viewing class labels as ``colors'', the random surfer is ``spraying'' different node types with different color palettes; hence the name Graffiti. We demonstrate the performance gains of our method by comparing it to three state-of-the-art techniques for graph-based classification.