Local Clustering in Provenance Graphs (Extended Version)

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 and selecting appropriate truncation points for returning an object’s ancestry or lineage. Generic graph clustering algorithms are not effective at producing semantically meaningful clusters in provenance graphs. We identify three key properties of provenance graphs and exploit them to justify two new centrality metrics we developed, specifically for use in performing local clustering on provenance graphs.