Local clustering in provenance graphs

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Local clustering in provenance graphs

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Title: Local clustering in provenance graphs
Author: Macko, Peter; Margo, Daniel Wyatt; Seltzer, Margo I.

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Citation: Macko, Peter, Daniel Margo, and Margo Seltzer. 2013. “Local Clustering in Provenance Graphs.” In Proceedings of the 22nd ACM International Conference on Conference on Information & Knowledge Management - CIKM ’13, October 27 - November 01, 2013, San Francisco, CA, 835-840. doi:10.1145/2505515.2505624.
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Abstract: Systems that capture and store data provenance, the record of how an object has arrived at its current state, accumulate historical metadata over time, forming a large graph. Local clustering in these graphs, in which we start with a seed vertex and grow a cluster around it, is of paramount importance because it supports critical provenance applications such as identifying semantically meaningful tasks in an object's history. However, generic graph clustering algorithms are not effective at these tasks. We identify three key properties of provenance graphs and exploit them to justify two new centrality metrics we developed for use in performing local clustering on provenance graphs.
Published Version: doi:10.1145/2505515.2505624
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:33921644
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