Department of Computer Science
Spectral decomposition for optimal graph index prediction
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.
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
Gold Coast, Australia
Link to Publisher's Edition
Song, Liyan, Yun Peng, Byron Choi, Jianliang Xu, and Bingsheng He. "Spectral decomposition for optimal graph index prediction." Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14-17, 2013, Proceedings, Part I (2013): 187-200.