Graph Summaries for Subgraph Frequency Estimation

  • Angela Maduko
  • Kemafor Anyanwu
  • Amit Sheth
  • Paul Schliekelman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5021)

Abstract

A fundamental problem related to graph structured databases is searching for substructures. One issue with respect to optimizing such searches is the ability to estimate the frequency of substructures within a query graph. In this work, we present and evaluate two techniques for estimating the frequency of subgraphs from a summary of the data graph. In the first technique, we assume that edge occurrences on edge sequences are position independent and summarize only the most informative dependencies. In the second technique, we prune small subgraphs using a valuation scheme that blends information about their importance and estimation power. In both techniques, we assume conditional independence to estimate the frequencies of larger subgraphs. We validate the effectiveness of our techniques through experiments on real and synthetic datasets.

Keywords

Frequency estimation Graph summaries Data summaries 

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Angela Maduko
    • 1
  • Kemafor Anyanwu
    • 2
  • Amit Sheth
    • 3
  • Paul Schliekelman
    • 4
  1. 1.Department of Computer ScienceUniversity of Georgia 
  2. 2.Department of Computer ScienceNorth Carolina State University 
  3. 3.Kno.e.sis CenterWright State University 
  4. 4.Department of StatisticsUniversity of Georgia 

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