Meta-path-based search and mining in heterogeneous information networks

Yizhou Sun, Jiawei Han

Research output: Contribution to journalArticlepeer-review


Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user's guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.

Original languageEnglish (US)
Article number06574671
Pages (from-to)329-338
Number of pages10
JournalTsinghua Science and Technology
Issue number4
StatePublished - Aug 2013


  • Heterogeneous information network
  • Meta-path
  • Relationship prediction
  • Similarity search
  • User-guided clustering

ASJC Scopus subject areas

  • General


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