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Mining heterogeneous information networks
- Source :
- ACM SIGKDD Explorations Newsletter. 14:20-28
- Publication Year :
- 2013
- Publisher :
- Association for Computing Machinery (ACM), 2013.
-
Abstract
- Most objects and data in the real world are of multiple types, interconnected, forming complex, heterogeneous but often semi-structured information networks. However, most network science researchers are focused on homogeneous networks, without distinguishing different types of objects and links in the networks. We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information networks. In this article, we summarize a set of methodologies that can effectively and efficiently mine useful knowledge from such information networks, and point out some promising research directions.
- Subjects :
- Point (typography)
Relational database
Computer science
Geography, Planning and Development
Network science
computer.software_genre
Data science
Set (abstract data type)
Homogeneous
General Earth and Planetary Sciences
Leverage (statistics)
Heterogeneous information
Data mining
computer
Water Science and Technology
Meaning (linguistics)
Subjects
Details
- ISSN :
- 19310153 and 19310145
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- ACM SIGKDD Explorations Newsletter
- Accession number :
- edsair.doi...........ef591f8c5106248d35c3a0039dd7a949