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  Multi-agent random walks for local clustering

Alamgir, M., & von Luxburg, U. (2010). Multi-agent random walks for local clustering. In IEEE International Conference on Data Mining (ICDM 2010) (pp. 18-27). Piscataway, NJ, USA: IEEE.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BD34-8 Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-BD35-6
Genre: Konferenzbeitrag

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 Urheber:
Alamgir, M1, Autor           
von Luxburg, U1, Autor           
Webb, Herausgeber
I., G., Herausgeber
Liu, B., Herausgeber
Zhang, C., Herausgeber
Gunopulos, D., Herausgeber
Wu, X., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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Schlagwörter: -
 Zusammenfassung: We consider the problem of local graph clustering where the aim is to discover the local cluster corresponding to a point of interest. The most popular algorithms to solve this problem start a random walk at the point of interest and let it run until some stopping criterion is met. The vertices visited are then considered the local cluster. We suggest a more powerful alternative, the multi-agent random walk. It consists of several “agents” connected by a fixed rope of length l. All agents move independently like a standard random walk on the graph, but they are constrained to have distance at most l from each other. The main insight is that for several agents it is harder to simultaneously travel over the bottleneck of a graph than for just one agent. Hence, the multi-agent random walk has less tendency to mistakenly merge two different clusters than the original random walk. In our paper we analyze the multi-agent random walk theoretically and compare it experimentally to the major local graph clustering algorithms from the literature. We find that our multi-agent random walk consistently outperforms these algorithms.

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Sprache(n):
 Datum: 2010-12
 Publikationsstatus: Erschienen
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: URI: http://datamining.it.uts.edu.au/icdm10/
DOI: 10.1109/ICDM.2010.87
BibTex Citekey: 6850
 Art des Abschluß: -

Veranstaltung

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Titel: IEEE International Conference on Data Mining (ICDM 2010)
Veranstaltungsort: Sydney, Australia
Start-/Enddatum: -

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Titel: IEEE International Conference on Data Mining (ICDM 2010)
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Piscataway, NJ, USA : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 18 - 27 Identifikator: -