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Getting lost in space: Large sample analysis of the resistance distance

MPG-Autoren
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von Luxburg,  U
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Radl,  A
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://papers.nips.cc/paper/3891-getting-lost-in-space-large-sample-analysis-of-the-resistance-distance.pdf
(Verlagsversion)

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Zitation

von Luxburg, U., Radl, A., & Hein, M. (2011). Getting lost in space: Large sample analysis of the resistance distance. Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, 2622-2630.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-BB74-8
Zusammenfassung
The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and back. We study the behavior of the commute distance as the size of the underlying graph increases. We prove that the commute distance converges to an expression that does not take into account the structure of the graph at all and that is completely meaningless as a distance function on the graph. Consequently, the use of the raw commute distance for machine learning purposes is strongly discouraged for large graphs and in high dimensions. As an alternative we introduce the amplified commute distance that corrects for the undesired large sample effects.