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GraphFind: enhancing graph searching by low support data mining techniques.

Alfredo Ferro1, Rosalba Giugno, Misael Mongiovì

  • 1Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it

BMC Bioinformatics
|May 9, 2008
PubMed
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GraphFind efficiently searches large graph databases for exact and approximate subgraph matches. This system offers advanced filtering and data storage, outperforming existing methods and reducing index space.

Area of Science:

  • Bioinformatics
  • Cheminformatics
  • Database Management
  • Graph Theory

Background:

  • Biomedical and chemical databases are rapidly expanding.
  • Graphs are a natural data model for these databases.
  • Efficient graph searching is crucial for data exploration.

Purpose of the Study:

  • Introduce GraphFind, a system for efficient graph searching.
  • Enable exact and approximate subgraph matching.
  • Improve data retrieval from large graph databases.

Main Methods:

  • Implemented efficient graph searching algorithms.
  • Incorporated advanced filtering techniques for approximate search.
  • Utilized effective data storage based on low-support data mining.

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Main Results:

  • GraphFind allows users to select candidate subgraphs.
  • The system demonstrates efficient approximate search capabilities.
  • Low-support data mining contributes to effective data storage.

Conclusions:

  • GraphFind outperforms Frowns, GraphGrep, and gIndex on large collections of small graphs.
  • The low-support mining technique significantly reduces index space.
  • GraphFind offers an effective solution for graph searching in large databases.