LGM: Mining Frequent Subgraphs from Linear Graphs

  • Yasuo Tabei
  • Daisuke Okanohara
  • Shuichi Hirose
  • Koji Tsuda
Conference paper

DOI: 10.1007/978-3-642-20847-8_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6635)
Cite this paper as:
Tabei Y., Okanohara D., Hirose S., Tsuda K. (2011) LGM: Mining Frequent Subgraphs from Linear Graphs. In: Huang J.Z., Cao L., Srivastava J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2011. Lecture Notes in Computer Science, vol 6635. Springer, Berlin, Heidelberg

Abstract

A linear graph is a graph whose vertices are totally ordered. Biological and linguistic sequences with interactions among symbols are naturally represented as linear graphs. Examples include protein contact maps, RNA secondary structures and predicate-argument structures. Our algorithm, linear graph miner (LGM), leverages the vertex order for efficient enumeration of frequent subgraphs. Based on the reverse search principle, the pattern space is systematically traversed without expensive duplication checking. Disconnected subgraph patterns are particularly important in linear graphs due to their sequential nature. Unlike conventional graph mining algorithms detecting connected patterns only, LGM can detect disconnected patterns as well. The utility and efficiency of LGM are demonstrated in experiments on protein contact maps.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yasuo Tabei
    • 1
  • Daisuke Okanohara
    • 2
  • Shuichi Hirose
    • 3
  • Koji Tsuda
    • 1
    • 3
  1. 1.ERATO Minato ProjectJapan Science and Technology AgencySapporoJapan
  2. 2.Preferred Infrastructure, IncTokyoJapan
  3. 3.Computational Biology Research CenterNational Institute of Advanced Industrial Science and Technology (AIST)TokyoJapan

Personalised recommendations