Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Spectral decomposition for optimal graph index prediction

Language

English

Abstract

There is an ample body of recent research on indexing for structural graph queries. However, as verified by our experiments with a large number of random and scale-free graphs, there may be a great variation in the performances of indexes of graph queries. Unfortunately, the structures of graph indexes are often complex and ad-hoc, so deriving an accurate performance model is a daunting task. As a result, database practitioners may encounter difficulties in choosing the optimal index for their data graphs. In this paper, we address this problem by proposing a spectral decomposition method for predicting the relative performances of graph indexes. Specifically, given a graph, we compute its spectrum. We then propose a similarity function to compare the spectrums of graphs. We adopt a classification algorithm to build a model and a voting algorithm for predicting the optimal index. Our empirical studies on a large number of random and scale-free graphs, using four structurally distinguishable indexes, demonstrate that our spectral decomposition method is robust and almost always exhibits an accuracy of 70% or above. © Springer-Verlag 2013.

Publication Date

2013

Source Publication Title

Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I

Start Page

187

End Page

200

Conference Location

Gold Coast, Australia

Publisher

Springer

DOI

10.1007/978-3-642-37453-1_16

ISBN (print)

9783642374524

ISBN (electronic)

9783642374531

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