Mining and indexing graphs for supergraph search

Dayu Yuan, Prasenjit Mitra, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingChapter

14 Citations (Scopus)


We study supergraph search (SPS), that is, given a query graph qand a graph database G that contains a collection of graphs, returngraphs that have q as a supergraph from G. SPS has broad applicationsin bioinformatics, cheminformatics and other scientific andcommercial fields. Determining whether a graph is a subgraph (orsupergraph) of another is an NP-complete problem. Hence, it is intractableto compute SPS for large graph databases. Two separateindexing methods, a "filter + verify"-based method and a "prefixsharing"-based method, have been studied to efficiently computeSPS. To implement the above two methods, subgraph patterns aremined from the graph database to build an index. Those subgraphsare mined to optimize either the filtering gain or the prefix-sharinggain. However, no single subgraph-mining algorithm considersboth gains. This work is the first one to mine subgraphs to optimize boththe filtering gain and the prefix-sharing gain while processing SPSqueries. First, we show that the subgraph-mining problem is NPhard. Then, we propose two polynomial-time algorithms to solvethe problem with an approximation ratio of 1-1/e and 1/4 respectively. In addition, we construct a lattice-like index, LW-index, toorganize the selected subgraph patterns for fast index-lookup. Ourexperiments show that our approach improves the query processingtime for SPS queries by a factor of 3 to 10.

Original languageEnglish
Title of host publicationProceedings of the VLDB Endowment
Number of pages12
Publication statusPublished - Aug 2013
Externally publishedYes


ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

Cite this

Yuan, D., Mitra, P., & Giles, C. L. (2013). Mining and indexing graphs for supergraph search. In Proceedings of the VLDB Endowment (10 ed., Vol. 6, pp. 829-840)