Learning to rank graphs for online similar graph search

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Many applications in structure matching require the ability to search for graphs that are similar to a query graph, i.e., similarity graph queries. Prior works, especially in chemoinformatics, have used the maximum common edge subgraph (MCEG) to compute the graph similarity. This approach is prohibitively slow for real-time queries. In this work, we propose an algorithm that extracts and indexes subgraph features from a graph dataset. It computes the similarity of graphs using a linear graph kernel based on feature weights learned offline from a training set generated using MCEG. We show empirically that our proposed algorithm of learning to rank graphs can achieve higher normalized discounted cumulative gain compared with existing optimal methods based on MCEG. The running time of our algorithm is orders of magnitude faster than these existing methods.

Original languageEnglish (US)
Title of host publicationACM 18th International Conference on Information and Knowledge Management, CIKM 2009
Pages1871-1874
Number of pages4
DOIs
StatePublished - 2009
EventACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong, China
Duration: Nov 2 2009Nov 6 2009

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

OtherACM 18th International Conference on Information and Knowledge Management, CIKM 2009
CountryChina
CityHong Kong
Period11/2/0911/6/09

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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