Fast best-effort pattern matching in large attributed graphs

Hanghang Tong, Christos Faloutsos, Brian Gallagher, Tina Eliassi-Rad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

162 Scopus citations

Abstract

We focus on large graphs where nodes have attributes, such as a social network where the nodes are labelled with each person's job title. In such a setting, we want to find subgraphs that match a user query pattern. For example, a "star" query would be, "find a CEO who has strong interactions with a Manager, a Lawyer,and an Accountant, or another structure as close to that as possible". Similarly, a "loop" query could help spot a money laundering ring. Traditional SQL-based methods, as well as more recent graph indexing methods, will return no answer when an exact match does not exist. This is the first main feature of our method. It can find exact-, as well as near-matches, and it will present them to the user in our proposed "goodness" order. For example, our method tolerates indirect paths between, say, the "CEO" and the "Accountant" of the above sample query, when direct paths don't exist. Its second feature is scalability. In general, if the query has n q nodes and the data graph has n nodes, the problem needs polynomial time complexity O(n n q), which is prohibitive. Our G-Ray ("Graph X-Ray") method finds high-quality subgraphs in time linear on the size of the data graph. Experimental results on the DLBP author-publication graph (with 356K nodes and 1.9M edges) illustrate both the effectiveness and scalability of our approach. The results agree with our intuition, and the speed is excellent. It takes 4 seconds on average fora 4-node query on the DBLP graph.

Original languageEnglish (US)
Title of host publicationKDD-2007
Subtitle of host publicationProceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages737-746
Number of pages10
DOIs
StatePublished - 2007
EventKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Jose, CA, United States
Duration: Aug 12 2007Aug 15 2007

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

OtherKDD-2007: 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
CountryUnited States
CitySan Jose, CA
Period8/12/078/15/07

Keywords

  • Attributed graph
  • Pattern match
  • Random walk

ASJC Scopus subject areas

  • Software
  • Information Systems

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