Discover hierarchical subgraphs with network-topology based ranking score

  • Authors:
  • Wookey Lee;Nidhi R. Arora;Tae-Chang Jo

  • Affiliations:
  • INHA University, Incheon, South Korea;INHA University, Incheon, South Korea;INHA University, Incheon, South Korea

  • Venue:
  • Proceedings of the Third C* Conference on Computer Science and Software Engineering
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

The number of applications to enable keyword query over graph-structured data are enormously increasing in various application domains like Web, Database, Chemical compounds, Bio-informatics etc. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources. In this paper, we focus on an efficient algorithm for keyword query processing over graph-structured data. The proposed algorithm integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. The key idea is to include link structure not only to enumerate the ranking score like PageRank algorithm but also for generating the result trees. The proposed algorithm generates answers in the order that is highly correlated with the desired ranking of the final result set. We also introduce a novel ranking measure called the Network Relevance Score (NRS) to enumerate the set of relevant result networks. NRS assigns score based on the combination of content and link structure of the result network. We conduct thorough experimental analysis and find out that NRS produces result trees in correlation with the desired ranking of the solution set which in turn results in higher user satisfaction.