Meta-Path-Based Search and Mining in Heterogeneous Information Networks

When: Monday, June 24, 2013 at 4:00 pm
Where: DA 5th fl
Speaker: Yizhou Sun
Organization: Northeastern University
Sponsor: CCNR


Meta-Path-Based Search and Mining in Heterogeneous Information Networks


Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks.  By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks.

Different from homogeneous information networks, where objects and links are treated either as of the same type or as of untyped nodes or links, heterogeneous information networks in our model are semi-structured and typed, following a network schema. The network schema provides a meta structure of the information network. We then propose to use meta-paths defined on network schemas to systematically define numerous semantic relationships across multiple types of objects. Meta-paths provide guidance of 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 information diffusion can be addressed by systematic exploration of the network meta structure. Results with better interpretation and higher accuracy are observed in many real-world information networks.