Query-driven discovery of semantically similar substructures in heterogeneous networks

Xiao Yu, Yizhou Sun, Peixiang Zhao, Jiawei Han

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

Abstract

Heterogeneous information networks that contain multiple types of objects and links are ubiquitous in the real world, such as bibliographic networks, cyber-physical networks, and social media networks. Although researchers have studied various data mining tasks in information networks, interactive query-based network exploration techniques have not been addressed systematically, which, in fact, are highly desirable for exploring large-scale information networks. In this demo, we introduce and demonstrate our recent research project on query-driven discovery of semantically similar substructures in heterogeneous networks. Given a subgraph query, our system searches a given large information network and finds efficiently a list of subgraphs that are structurally identical and semantically similar. Since data mining methods are used to obtain semantically similar entities (nodes), we use discovery as a term to describe this process. In order to achieve high efficiency and scalability, we design and implement a filter-and verification search framework, which can first generate promising subgraph candidates using off line indices built by data mining results, and then verify candidates with a recursive pruning matching process. The proposed system demonstrates the effectiveness of our query-driven semantic similarity search framework and the efficiency of the proposed methodology on multiple real-world heterogeneous information networks.

Original languageEnglish (US)
Title of host publicationKDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages1500-1503
Number of pages4
DOIs
StatePublished - Sep 14 2012
Event18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012 - Beijing, China
Duration: Aug 12 2012Aug 16 2012

Publication series

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

Other

Other18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012
CountryChina
CityBeijing
Period8/12/128/16/12

Keywords

  • data mining
  • graph query
  • information network

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint Dive into the research topics of 'Query-driven discovery of semantically similar substructures in heterogeneous networks'. Together they form a unique fingerprint.

  • Cite this

    Yu, X., Sun, Y., Zhao, P., & Han, J. (2012). Query-driven discovery of semantically similar substructures in heterogeneous networks. In KDD'12 - 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1500-1503). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). https://doi.org/10.1145/2339530.2339765