Approximate graph mining with label costs

Pranay Anchuri, Mohammed J. Zaki, Omer Barkol, Shahar Golan, Moshe Shamy

Research output: Contribution to journalArticle

Abstract

Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to compute some cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that the mined approximate patterns yield interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.

Original languageEnglish
JournalHP Laboratories Technical Report
Issue number36
Publication statusPublished - 24 Jun 2013

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Keywords

  • Approximation techniques
  • CMDB
  • Data mining
  • Graph analysis

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Anchuri, P., Zaki, M. J., Barkol, O., Golan, S., & Shamy, M. (2013). Approximate graph mining with label costs. HP Laboratories Technical Report, (36).